diff --git a/.github/workflows/main.yaml b/.github/workflows/main.yaml new file mode 100644 index 00000000..8844eafe --- /dev/null +++ b/.github/workflows/main.yaml @@ -0,0 +1,114 @@ +name: Tests + +on: + push: + branches: + - smk_workflow_catalog + # paths: + # - "github-actions-runner/Dockerfile" + +jobs: + build_container: + name: Build and push image + runs-on: ubuntu-20.04 + env: + IMAGE_NAME: mosaicatcher-pipeline + + if: github.ref == 'refs/heads/master' + steps: + - uses: actions/checkout@v2 + + - name: Read upstream tag without version + id: gettag + run: echo "::set-output name=tag::$(head -n 1 github-actions-runner/Dockerfile | awk -F':' '{print $2}' | awk -F'-' 'BEGIN { OFS="-" } {$NF=""; print $0}')" + + - name: Read internal update version + id: getversion + run: echo "::set-output name=version::$(grep 'ARG RUNNER_VERSION' github-actions-runner/Dockerfile | awk -F'=' '{print $2}')" + + - name: Build Image + id: build-image + uses: redhat-actions/buildah-build@v2 + with: + image: ${{ env.IMAGE_NAME }} + tags: latest dev 1.3 + dockerfiles: | + ./github-actions-runner/Dockerfile + + - name: Push To DockerHub + id: push-to-dockerhub + uses: redhat-actions/push-to-registry@v2 + with: + image: ${{ steps.build-image.outputs.image }} + tags: ${{ steps.build-image.outputs.tags }} + registry: docker.io/weber8thomas + username: ${{ secrets.DOCKER_USERNAME }} + password: ${{ secrets.DOCKER_TOKEN }} + + - name: Use the image + run: echo "New images has been pushed to ${{ steps.push-to-quay.outputs.registry-paths }}" + # jobs: + test_workflow: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + # - name: Setup Snakemake environment + # run: | + # export PATH="/usr/share/miniconda/bin:$PATH" + # conda config --set channel_priority strict + # conda install -c conda-forge -q mamba + # # ensure that mamba is happy to write into the cache + # sudo chown -R runner:docker /usr/share/miniconda/pkgs/cache + # # additionally add singularity + # # TODO remove version constraint: needed because 3.8.7 fails with missing libz: + # # bin/unsquashfs: error while loading shared libraries: libz.so.1: cannot open shared object file: No such file or directory + # # mamba create -y -n mosaicatcher_env -c conda-forge -c bioconda snakemake pandas pysam tqdm imagemagick "singularity<=3.8.6" + # # source activate mosaicatcher_env + # # conda list + # # which python + # # python -c 'import pysam; print(pysam)' + - name: Downloading data + uses: snakemake/snakemake-github-action@v1.22.0 + with: + directory: .test + snakefile: Snakefile + stagein: "mamba env remove -n snakemake && mamba create -y -n snakemake -c conda-forge -c bioconda unzip snakemake pandas pysam tqdm imagemagick && source activate snakemake" + args: "--cores 1 --config mode=download_data dl_external_files=True dl_bam_example=True input_bam_location=TEST_EXAMPLE_DATA/" + - name: Test data + uses: snakemake/snakemake-github-action@v1.22.0 + with: + directory: .test + snakefile: Snakefile + stagein: 'mamba env remove -n snakemake && mamba create -y -n snakemake -c conda-forge -c bioconda snakemake pandas pysam tqdm imagemagick "singularity<=3.8.6" && source activate snakemake && ls -lh' + args: "--cores 1 --config plot=True input_bam_location=TEST_EXAMPLE_DATA/ output_location=TEST_OUTPUT/ --use-conda --use-singularity" + + formatting: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + - name: Formatting + uses: github/super-linter@v3.16.1 + env: + VALIDATE_ALL_CODEBASE: false + DEFAULT_BRANCH: smk_workflow_catalog + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + VALIDATE_SNAKEMAKE_SNAKEFMT: true + + linting: + runs-on: ubuntu-latest + steps: + - uses: actions/checkout@v1 + - name: Downloading data + uses: snakemake/snakemake-github-action@v1.22.0 + with: + directory: .test + snakefile: Snakefile + stagein: "mamba env remove -n snakemake && mamba create -y -n snakemake -c conda-forge -c bioconda unzip snakemake pandas pysam tqdm imagemagick && source activate snakemake && ls -l && pwd" + args: "--cores 1 --config mode=download_data dl_external_files=True dl_bam_example=True input_bam_location=TEST_EXAMPLE_DATA/ --touch" + - name: Linting + uses: snakemake/snakemake-github-action@v1.22.0 + with: + directory: ".test" + snakefile: Snakefile + stagein: "mamba env remove -n snakemake && mamba create -y -n snakemake -c conda-forge -c bioconda unzip snakemake pandas pysam tqdm imagemagick && source activate snakemake && ls -l && pwd" + args: "--lint" diff --git a/.gitignore b/.gitignore new file mode 100644 index 00000000..a18dcfe7 --- /dev/null +++ b/.gitignore @@ -0,0 +1,87 @@ +# Hidden folders & files +.DS_Store +.vscode/ +.snakemake/ +.panoptes.db +._.DS_Store +.pytest_cache/ +.condarc +files.txt +workflow/.conda/ + +# Tmp files & execution outputs +*.pyc +*.zip +*.gz +*.db +*.png +*.pdf +*.svg +*.tsv +*.csv + +# Links +*@ + +# Mosaicatcher folders +chroms/ +counts/ +log/ +plots/ +segmentation/ +segmentation2/ +snv_calls/ +strand_states/ +sv_probabilities/ +workflow/config/config_df.tsv +workflow/config/exclude_file.txt +workflow/config/exclude_file + +# Docs +docs/build/ +build/ +*.html +workflow/static/ + +# Python +__pycache__ +workflow/scripts/__pycache__ + +# Zenodo +workflow/sandbox.zenodo.org/ +sandbox.zenodo.org/ +TEST_OUTPUT/ +TEST_OUTPUT +workflow/test.txt +.snakemake + +# Exceptions +!docs/images/*.png +!workflow/data/segdups/segDups_hg38_UCSCtrack.bed.gz +!workflow/data/bin_200kb_all.bed +!config/config.yaml +!config/* + +# Dev +discover_big_files_git.sh +builds/ +workflow/report_TALL/ +*.bam +*.bai +workflow/TEST_EXAMPLE_DATA/ +TEST_EXAMPLE_DATA +TEST_EXAMPLE_DATA/ +workflow/logs/ +workflow/errors/ + +# git + +## Others +# afac/ +# workflow/.snakemake +# bam/ + +## Personal note: files/folders specific to dev branch +# .gitlab-ci.yml // to use with LFS example data in dev branch +# singularity/ folder +# afac/ debugging & dev folder diff --git a/.snakemake-workflow-catalog.yml b/.snakemake-workflow-catalog.yml new file mode 100644 index 00000000..69c641ac --- /dev/null +++ b/.snakemake-workflow-catalog.yml @@ -0,0 +1,18 @@ +# configuration of display in snakemake workflow catalog: https://snakemake.github.io/snakemake-workflow-catalog + +usage: + mandatory-flags: # optional definition of additional flags + desc: # describe your flags here in a few sentences (they will be inserted below the example commands) + flags: + - "snakemake" + - "mosaicatcher" + - "single-cell-genomics" + - "strand-seq" + - "structural-variants" + - "sv-calling" + # put your flags here + software-stack-deployment: # definition of software deployment method (at least one of conda, singularity, or singularity+conda) + conda: false # whether pipeline works with --use-conda + singularity: false # whether pipeline works with --use-singularity + singularity+conda: true # whether pipeline works with --use-singularity --use-conda + report: true # add this to confirm that the workflow allows to use 'snakemake --report report.zip' to generate a report containing all results and explanations diff --git a/CHANGELOG.md b/CHANGELOG.md new file mode 100644 index 00000000..fc37c3a2 --- /dev/null +++ b/CHANGELOG.md @@ -0,0 +1,26 @@ +## 1.3 (2022-06-02) + +* Check if SM tag are corresponding to folder name [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/a4611b70a03675ee5db7816728b28eb9a9875e5c) + + + +## 1.2.3 (2022-05-18) + +* Correct issue [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/932d2529815cc31a57f60ca860fadf65212738f4) +* Small correction [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/5ed61ec9d20692d4e14394baea5636a21ae9dfc1) +* Correct SMK download BAM example files [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/d84904a5c1ec9f4901f7dc69d7b879692c1266c6) +* Update README.md [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/881c6612b31e74efbb854b85d4e5328e300e7c2e) + + +## 1.2.2 (2022-05-18) + + +* Handle of multi samples in the same folder now Change the way to retrieve the selected cell list [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/3f0dc28ec22d88def269c215ef551800b8b1f7e5) + + +## 1.2.1 (2022-05-17) + +* Removing files .gitattributes .gitlab-ci.yml [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/b6a46ff3d7dd8978743be9c4ee801535aac03eab) +* Download example & external data Implemented rules based on snakemake.remote.HTTP function that can be called through config.yaml / CLI arguments Update config.yaml file Update rules/examples.smk Update Snakefile Update README.md [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/a835f79928bf6ec5c5b93678bd89bc54c59e3206) + + diff --git a/LICENSE b/LICENSE new file mode 100644 index 00000000..28819619 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2022 Thomas Weber (thomas.weber@embl.de) + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 00000000..c0626741 --- /dev/null +++ b/README.md @@ -0,0 +1,114 @@ +![MosaiCatcher](docs/images/mosaic_logo.png) + + +Structural variant calling from single-cell Strand-seq data [Snakemake](https://github.com/snakemake/snakemake) pipeline. + + +# Overview of this workflow + +This workflow uses [Snakemake](https://github.com/snakemake/snakemake) to +execute all steps of MosaiCatcher in order. The starting point are single-cell +BAM files from Strand-seq experiments and the final output are SV predictions in +a tabular format as well as in a graphical representation. To get to this point, +the workflow goes through the following steps: + + 1. Binning of sequencing reads in genomic windows of 100kb via [mosaic](https://github.com/friendsofstrandseq/mosaicatcher) + 2. Strand state detection + 3. [Optional]Normalization of coverage with respect to a reference sample + 4. Multi-variate segmentation of cells ([mosaic](https://github.com/friendsofstrandseq/mosaicatcher)) + 5. Haplotype resolution via [StrandPhaseR](https://github.com/daewoooo/StrandPhaseR) + 6. Bayesian classification of segmentation to find SVs using MosaiClassifier + 7. Visualization of results using custom R plots + + +# Quick Start + +1. Install [Singularity](https://www.sylabs.io/guides/3.0/user-guide/) +2. To prevent conda channel errors +``` +conda config --set channel_priority +``` +3. Create a dedicated conda environment +``` +conda create -n mosaicatcher_env -c conda-forge -c bioconda snakemake pandas pysam imagemagick tqdm && conda activate mosaicatcher_env +``` +4. Clone the repository +``` +git clone https://github.com/friendsofstrandseq/mosaicatcher-pipeline.git && cd mosaicatcher-pipeline +``` +5. Download test and reference data +``` +snakemake -c1 --config mode=download_data dl_external_files=True dl_bam_example=True input_bam_location=TEST_EXAMPLE_DATA/ +``` +6. Run on example data on only one small chromosome (`` must be replaced by your disk letter/name, `/g` or `/scratch` at EMBL for example) +``` +snakemake --cores 12 --config mode=mosaiclassifier plot=True input_bam_location=TEST_EXAMPLE_DATA/ output_location=TEST_OUTPUT/ chromosomes="[chr21]" --use-conda --use-singularity --singularity-args "-B /:/" --latency-wait 60 +``` + +7. Generate report on example data +``` +snakemake --cores 12 --config mode=mosaiclassifier plot=True input_bam_location=TEST_EXAMPLE_DATA/ output_location=TEST_OUTPUT/ chromosomes="[chr21]" --use-conda --use-singularity --singularity-args "-B /:/" --latency-wait 60 --report +``` + + +8. Start running your own analysis +``` +snakemake --cores 12 --config mode=mosaiclassifier plot=True input_bam_location= output_location= --use-conda --use-singularity --singularity-args "-B /:/" --latency-wait 60 + +``` +9. Generate report +``` +snakemake --cores 12 --config mode=mosaiclassifier plot=True input_bam_location= output_location= --use-conda --use-singularity --singularity-args "-B /:/" --latency-wait 60 --report +``` + + + + +# Documentation + +* [Usage](docs/Usage.md) +* [Parameters & input](docs/Parameters.md) +* [Output](docs/Output.md) (#TODO) + + + +# πŸ“† Roadmap + +## Technical-related features + +- [x] Zenodo automatic download of external files + indexes ([1.2.1](https://github.com/friendsofstrandseq/mosaicatcher-pipeline/releases/tag/1.2.1)) +- [x] Multiple samples in the parent folder ([1.2.2](https://github.com/friendsofstrandseq/mosaicatcher-pipeline/releases/tag/1.2.2)) +- [x] Automatic testing of BAM SM tag compared to sample folder name ([1.2.3](https://github.com/friendsofstrandseq/mosaicatcher-pipeline/releases/tag/1.2.3)) +- [x] On-error/success e-mail ([1.3](https://github.com/friendsofstrandseq/mosaicatcher-pipeline/releases/tag/1.3)) +- [x] HPC execution (slurm profile for the moment) ([1.3](https://github.com/friendsofstrandseq/mosaicatcher-pipeline/releases/tag/1.3)) +- [ ] Plotting options (enable/disable segmentation back colors) +- [ ] Full singularity image with preinstalled conda envs +- [ ] Portable Encapsulated Project compliant +- [ ] Single BAM folder with side config file +## Bioinformatic-related features + +- [ ] Change of reference genome (currently only GRCh38) +- [ ] Upstream QC pipeline and FastQ handle +- [ ] Pooling samples +- [ ] Self-handling of low-coverage cells + +## Small issues to fix + +- [ ] Move pysam / SM tag comparison script to snakemake rule + + +# πŸ›‘ Troubleshooting & Current limitations + +- Do not change the structure of your input folder after running the pipeline, first execution will build a config dataframe file (`OUTPUT_DIRECTORY/config/config.tsv`) that contains the list of cells and the associated paths +- Do not change the list of chromosomes after a first execution (i.e: first execution using `count` mode on `chr21`, second execution using `segmentation` mode on all chromosomes) +- ~~Pipeline is unstable on **male** samples (LCL sample for example) for the moment due to the impossibility to run strandphaser (only one haplotype for the X chrom)~~ That was solved based on [Hufsah Ashraf](https://github.com/orgs/friendsofstrandseq/people/Hufsah-Ashraf) and [Wolfram HΓΆps](https://github.com/orgs/friendsofstrandseq/people/WHops) work allowing to determine automatically sample sex and use [snakemake checkpoint](https://snakemake.readthedocs.io/en/stable/snakefiles/rules.html#data-dependent-conditional-execution) that allow data-depdendent conditional execution. Thus, initial list of chromosomes was updated regarding the samples sex in order to bypass chrX & chrY for male sample, as both are present in a single haplotype. + + +# πŸ“• References + + +> Strand-seq publication: Falconer, E., Hills, M., Naumann, U. et al. DNA template strand sequencing of single-cells maps genomic rearrangements at high resolution. Nat Methods 9, 1107–1112 (2012). https://doi.org/10.1038/nmeth.2206 + +> scTRIP/MosaiCatcher original publication: Sanders, A.D., Meiers, S., Ghareghani, M. et al. Single-cell analysis of structural variations and complex rearrangements with tri-channel processing. Nat Biotechnol 38, 343–354 (2020). https://doi.org/10.1038/s41587-019-0366-x + + diff --git a/Snakefile b/Snakefile new file mode 100644 index 00000000..e03e01e1 --- /dev/null +++ b/Snakefile @@ -0,0 +1,221 @@ +print("###################################") +print("# MOSAICATCHER SNAKEMAKE PIPELINE #") +print("###################################") + +import math +from collections import defaultdict +import pandas as pd +import os, sys +from pprint import pprint +from snakemake.utils import min_version + +##### set minimum snakemake version ##### +min_version("7.4.1") + + +# TODO : check config/config CLI ... +# configfile: "../config/config.yaml" +configfile: "config/config.yaml" +# print(config) + +mode_selected = config["mode"].lower() +correct_modes = ["count", "segmentation", "mosaiclassifier", "download_data"] +assert mode_selected in correct_modes, "Wrong mode selected : {}\nFollowing list of modes are available : {}".format(config["mode"], ", ".join(correct_modes)) + +plot_option_selected = config["plot"] +assert type(plot_option_selected) is bool, "Wrong plot option selected : {}\nPlease enter a valid value (True / False)".format(config["plot"]) + +dl_bam_example_option_selected = config["dl_bam_example"] +assert type(dl_bam_example_option_selected) is bool, "Wrong plot option selected : {}\nPlease enter a valid value (True / False)".format(config["plot"]) + +dl_external_files_option_selected = config["dl_external_files"] +assert type(dl_external_files_option_selected) is bool, "Wrong plot option selected : {}\nPlease enter a valid value (True / False)".format(config["plot"]) + + +print("Mode selected : {}".format(mode_selected)) +print("Plots output enabled : {}".format(config["plot"])) + + +print('Input folder selected : {}'.format(config['input_bam_location'])) +print('Output folder selected : {}'.format(config['output_location'])) + +if config["output_location"].endswith("/") is False: + config["output_location"] += "/" + # print("/ was missing at end of output folder, continuing ...") + +if config["input_bam_location"].endswith("/") is False: + config["input_bam_location"] += "/" + # print("/ was missing at end of input folder, continuing ...") + + + +report: "workflow/report/workflow.rst" + + +# TODO: commit only in another branch to test +# # this container defines the underlying OS for each job when using the workflow +# # with --use-conda --use-singularity +# container: "docker://condaforge/mambaforge:4.12.0-0" +containerized: "docker://weber8thomas/mosaicatcher-pipeline:dev" + +# include: "rules/input_check.smk" +# rule all: +# input: +# rules.check_bam_input.output + + +if config["mode"] != "download_data": + if os.path.isfile(config["output_location"] + "config/config_df.tsv") is False: + from workflow.scripts.utils import handle_input + c = handle_input.HandleInput( + input_path=config["input_bam_location"], + output_path=config["output_location"] + "config/config_df.tsv", + check_sm_tag=config["check_sm_tag"] + ) + df_config_files = c.df_config_files + else: + df_config_files = pd.read_csv(config["output_location"] + "config/config_df.tsv", sep="\t") + # print(df_config_files) + # exit() + + + + + ###################################################################### + + # TODO : move to somewhere else + + methods = [ + "simpleCalls_llr4_poppriorsTRUE_haplotagsTRUE_gtcutoff0_regfactor6_filterFALSE", + "simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE", + ] + + ###################################################################### + + # IMPORT SMK RULES + + include: "workflow/rules/input_check.smk" + include: "workflow/rules/setup.smk" + include: "workflow/rules/count.smk" + include: "workflow/rules/segmentation.smk" + include: "workflow/rules/plots.smk" + include: "workflow/rules/regenotyping.smk" + include: "workflow/rules/utils.smk" + include: "workflow/rules/strandphaser.smk" + include: "workflow/rules/haplotagging.smk" + include: "workflow/rules/mosaiclassifier.smk" + include: "workflow/rules/postprocessing.smk" + include: "workflow/rules/stats.smk" + + +include: "workflow/rules/examples.smk" + +# Global wildcard constraints for consistent naming +# wildcard_constraints: + + + +# IF PLOT OPTION ENABLED, BUILD TMP DICT TO CALL OUTPUT +if plot_option_selected == True: + dict_cells_nb_per_sample = df_config_files.loc[df_config_files["Selected"] == True].groupby("Sample")["Cell"].nunique().to_dict() + tmp_dict = df_config_files.loc[df_config_files["Selected"] == True, ["Sample", "Cell"]].groupby("Sample")["Cell"].apply(lambda r: sorted(list(r))).to_dict() + tmp_dict = {s:{i+1:c for i,c in enumerate(cell_list)} for s,cell_list in tmp_dict.items()} + for s in tmp_dict.keys(): + tmp_dict[s][0] = "SummaryPage" + + + + + + + + + +###################### +# MODES OF EXECUTION # +###################### + + + + + +# MODE MOSAIC COUNT +if mode_selected == "count": + if plot_option_selected == True: + rule all: + input: + [config["output_location"] + "plots/{}/counts/{}.{}.pdf".format(sample, tmp_dict[sample][i], i) for sample in samples for i in range(dict_cells_nb_per_sample[sample] + 1)], + + elif plot_option_selected == False: + rule all: + input: + [config["output_location"] + "counts/{}/{}.txt.fixme.gz".format(sample, sample) for sample in samples] + +# MODE MOSAIC SEGMENTATION +elif mode_selected == "segmentation": + + if plot_option_selected == True: + rule all: + input: + [config["output_location"] + "plots/{}/counts/{}.{}.pdf".format(sample, tmp_dict[sample][i], i) for sample in samples for i in range(dict_cells_nb_per_sample[sample] + 1)], + [config["output_location"] + "segmentation/{}/Selection_initial_strand_state".format(sample) for sample in samples] + + elif plot_option_selected == False: + rule all: + input: + [config["output_location"] + "segmentation/{}/Selection_initial_strand_state".format(sample) for sample in samples] + +# MODE MOSAIC CLASSIFIER +elif mode_selected == "mosaiclassifier": + if plot_option_selected == True: + rule all: + input: + rules.install_rlib_strandphaser.output, + [config["output_location"] + "plots/{}/counts/{}.{}.pdf".format(sample, tmp_dict[sample][i], i) for sample in samples for i in range(dict_cells_nb_per_sample[sample] + 1)], + [config["output_location"] + "mosaiclassifier/sv_calls/{}/{}.tsv".format(sample, m) for sample in samples for m in methods], + expand(config["output_location"] + "plots/{sample}/sv_calls/{method}.{chrom}.pdf", sample = samples, chrom = config["chromosomes"], method = methods), + expand(config["output_location"] + "plots/{sample}/sv_consistency/{method}.consistency-barplot-{plottype}.pdf", sample = samples, method = methods, plottype = ["byaf","bypos"]), + expand(config["output_location"] + "plots/{sample}/sv_clustering/{method}-{plottype}.pdf", sample = samples, method = methods, plottype = ["position", "chromosome"]), + expand(config["output_location"] + "stats/{sample}/stats-merged.tsv", sample = samples), + + + elif plot_option_selected == False: + rule all: + input: + rules.install_rlib_strandphaser.output, + [config["output_location"] + "mosaiclassifier/sv_calls/{}/{}.tsv".format(sample, m) for sample in samples for m in methods], + [config["output_location"] + "mosaiclassifier/sv_calls/{}/{}.complex.tsv".format(sample, m) for sample in samples for m in methods], + expand(config["output_location"] + "stats/{sample}/stats-merged.tsv", sample = samples), + + + +# # TEST MODE +elif mode_selected == "download_data": + if dl_bam_example_option_selected is True and dl_external_files_option_selected is True: + rule all: + input: + rules.dl_example_data.output, + rules.dl_external_data.output, + rules.dl_external_data_index.output + + if dl_bam_example_option_selected is True and dl_external_files_option_selected is False: + rule all: + input: + rules.dl_example_data.output, + + if dl_bam_example_option_selected is False and dl_external_files_option_selected is True: + rule all: + input: + rules.dl_external_data.output, + rules.dl_external_data_index.output + + +if config["mail"]: + + onsuccess: + print("Workflow finished, no error") + shell('mail -s "Workflow finished, no error"' + config["mail"] + '< {log}') + + onerror: + print("An error occurred") + shell('mail -s "Workflow failed, an error occurred"' + config["mail"] + '< {log}') diff --git a/afac/StrandPhaseR_pipeline.R b/afac/StrandPhaseR_pipeline.R new file mode 100644 index 00000000..4dba4b74 --- /dev/null +++ b/afac/StrandPhaseR_pipeline.R @@ -0,0 +1,19 @@ +args <- commandArgs(TRUE) + +# add user defined path to load needed libraries +# .libPaths(c(.libPaths(), args[6])) + +# library(StrandPhaseR) +library(devtools) + +# source("/g/korbel2/weber/Gits/StrandPhaseR/R/StrandPhase.R") +load_all("/g/korbel2/weber/Gits/StrandPhaseR/") + + +strandPhaseR( + inputfolder = "/g/korbel2/weber/MosaiCatcher_files/LCL_TALL/H2NCTAFX2_GM20509B_20s000579-1-1/selected", + outputfolder = "/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/strandphaser/H2NCTAFX2_GM20509B_20s000579-1-1/StrandPhaseR_analysis.chrX", + configfile = "/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/strandphaser/H2NCTAFX2_GM20509B_20s000579-1-1/StrandPhaseR.chrX.config", + WCregions = "/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/strandphaser/H2NCTAFX2_GM20509B_20s000579-1-1/strandphaser_input.txt", + positions = "/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/snv_genotyping/H2NCTAFX2_GM20509B_20s000579-1-1/chrX.vcf", +) \ No newline at end of file diff --git a/afac/change-log-builder.sh b/afac/change-log-builder.sh new file mode 100644 index 00000000..6055b7bc --- /dev/null +++ b/afac/change-log-builder.sh @@ -0,0 +1,12 @@ +#!/usr/bin/env bash +previous_tag=0 +for current_tag in $(git tag --sort=-creatordate); do + + if [ "$previous_tag" != 0 ]; then + tag_date=$(git log -1 --pretty=format:'%ad' --date=short ${previous_tag}) + printf "## ${previous_tag} (${tag_date})\n\n" + git log ${current_tag}...${previous_tag} --pretty=format:'* %s [View](https://git.embl.de/tweber/mosaicatcher-update/-/commit/%H)' --reverse | grep -v Merge + printf "\n\n" + fi + previous_tag=${current_tag} +done diff --git a/afac/chrxy_analysis.ipynb b/afac/chrxy_analysis.ipynb new file mode 100644 index 00000000..031748c3 --- /dev/null +++ b/afac/chrxy_analysis.ipynb @@ -0,0 +1,977 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import os, sys" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chrX/chrY coverage ratioCellSampleXX/XY
01.423089GM20509Bx01PE20561LCL (XY)XY
11.344939GM20509Bx01PE20514LCL (XY)XY
21.324621GM20509Bx01PE20531LCL (XY)XY
31.353005GM20509Bx01PE20510LCL (XY)XY
40.911065GM20509Bx01PE20545LCL (XY)XY
...............
4219.793397BM510x04_PE20395RPE-BM510 (XX)XX
42210.187812BM510x04_PE20311RPE-BM510 (XX)XX
42310.093260BM510x04_PE20345RPE-BM510 (XX)XX
4247.219951BM510x04_PE20380RPE-BM510 (XX)XX
4259.454516BM510x04_PE20330RPE-BM510 (XX)XX
\n", + "

426 rows Γ— 4 columns

\n", + "
" + ], + "text/plain": [ + " chrX/chrY coverage ratio Cell Sample XX/XY\n", + "0 1.423089 GM20509Bx01PE20561 LCL (XY) XY\n", + "1 1.344939 GM20509Bx01PE20514 LCL (XY) XY\n", + "2 1.324621 GM20509Bx01PE20531 LCL (XY) XY\n", + "3 1.353005 GM20509Bx01PE20510 LCL (XY) XY\n", + "4 0.911065 GM20509Bx01PE20545 LCL (XY) XY\n", + ".. ... ... ... ...\n", + "421 9.793397 BM510x04_PE20395 RPE-BM510 (XX) XX\n", + "422 10.187812 BM510x04_PE20311 RPE-BM510 (XX) XX\n", + "423 10.093260 BM510x04_PE20345 RPE-BM510 (XX) XX\n", + "424 7.219951 BM510x04_PE20380 RPE-BM510 (XX) XX\n", + "425 9.454516 BM510x04_PE20330 RPE-BM510 (XX) XX\n", + "\n", + "[426 rows x 4 columns]" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "l = [pd.read_csv(file, sep=\"\\t\") for file in os.listdir(\".\") if file.endswith(\".tsv\")]\n", + "df = pd.concat(l)\n", + "d_xy = {\"C7_data\" : \"XX\", \"H2NCTAFX2_GM20509B_20s000579-1-1\" : \"XY\", \"RPE-BM510\":\"XX\", \"RPE1-WT\" : \"XX\"}\n", + "df = df.rename(columns={\"chrX/chrY_ratio\" :\"chrX/chrY coverage ratio\"})\n", + "df[\"XX/XY\"] = df[\"Sample\"].map(d_xy)\n", + "\n", + "df[\"Sample\"] = df[\"Sample\"].replace(\"H2NCTAFX2_GM20509B_20s000579-1-1\", \"LCL\")\n", + "df[\"Sample\"] = df[\"Sample\"] + \" (\" + df[\"XX/XY\"] + \")\"\n", + "\n", + "df = df.drop([\"Unnamed: 0\"], axis=1).reset_index(drop=True)\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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XX/XYXXXY
SampleC7_data (XX)RPE-BM510 (XX)RPE1-WT (XX)LCL (XY)
chrX/chrY coverage ratiocount1541458047
mean111081
std2111
min7751
25%10981
50%101091
75%121091
max1815116
\n", + "
" + ], + "text/plain": [ + "XX/XY XX \\\n", + "Sample C7_data (XX) RPE-BM510 (XX) RPE1-WT (XX) \n", + "chrX/chrY coverage ratio count 154 145 80 \n", + " mean 11 10 8 \n", + " std 2 1 1 \n", + " min 7 7 5 \n", + " 25% 10 9 8 \n", + " 50% 10 10 9 \n", + " 75% 12 10 9 \n", + " max 18 15 11 \n", + "\n", + "XX/XY XY \n", + "Sample LCL (XY) \n", + "chrX/chrY coverage ratio count 47 \n", + " mean 1 \n", + " std 1 \n", + " min 1 \n", + " 25% 1 \n", + " 50% 1 \n", + " 75% 1 \n", + " max 6 " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby([\"XX/XY\", \"Sample\"]).describe().T.round(0).astype(int)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "sns.set_theme()\n", + "plt.rcParams['figure.facecolor'] = 'white'\n", + "plt.rcParams['figure.dpi'] = 150\n", + "#plt.figure(figsize=(30,30))\n", + "g = sns.displot(df.sort_values(by=\"XX/XY\"), x=\"chrX/chrY coverage ratio\", hue=\"Sample\", binwidth=0.5)\n", + "\n", + "\n", + "\n", + "sns.move_legend(\n", + " g, \"upper center\",\n", + " ncol=4, title=None, frameon=False, fontsize=8, bbox_to_anchor=(0.5, 1.05)\n", + ")\n", + "plt.xlabel(\"Ratio\")\n", + "plt.ylabel(\"Density\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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chromstartendsamplecellcwclass
0chr10100000C7_dataC7x02PE2030111WW
1chr1100000200000C7_dataC7x02PE2030103WW
2chr1200000300000C7_dataC7x02PE2030100WW
3chr1300000400000C7_dataC7x02PE2030100WW
4chr1400000500000C7_dataC7x02PE2030100WW
...........................
1425082chrX155600000155700000H2NCTAFX2_GM20509B_20s000579-1-1GM20509Bx01PE2058904WW
1425083chrX155700000155800000H2NCTAFX2_GM20509B_20s000579-1-1GM20509Bx01PE2058934WW
1425084chrX155800000155900000H2NCTAFX2_GM20509B_20s000579-1-1GM20509Bx01PE2058935WW
1425085chrX155900000156000000H2NCTAFX2_GM20509B_20s000579-1-1GM20509Bx01PE2058954WW
1425086chrX156000000156040895H2NCTAFX2_GM20509B_20s000579-1-1GM20509Bx01PE2058900WW
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12886425 rows Γ— 8 columns

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" + ], + "text/plain": [ + " chrom start end sample \\\n", + "0 chr1 0 100000 C7_data \n", + "1 chr1 100000 200000 C7_data \n", + "2 chr1 200000 300000 C7_data \n", + "3 chr1 300000 400000 C7_data \n", + "4 chr1 400000 500000 C7_data \n", + "... ... ... ... ... \n", + "1425082 chrX 155600000 155700000 H2NCTAFX2_GM20509B_20s000579-1-1 \n", + "1425083 chrX 155700000 155800000 H2NCTAFX2_GM20509B_20s000579-1-1 \n", + "1425084 chrX 155800000 155900000 H2NCTAFX2_GM20509B_20s000579-1-1 \n", + "1425085 chrX 155900000 156000000 H2NCTAFX2_GM20509B_20s000579-1-1 \n", + "1425086 chrX 156000000 156040895 H2NCTAFX2_GM20509B_20s000579-1-1 \n", + "\n", + " cell c w class \n", + "0 C7x02PE20301 1 1 WW \n", + "1 C7x02PE20301 0 3 WW \n", + "2 C7x02PE20301 0 0 WW \n", + "3 C7x02PE20301 0 0 WW \n", + "4 C7x02PE20301 0 0 WW \n", + "... ... .. .. ... \n", + "1425082 GM20509Bx01PE20589 0 4 WW \n", + "1425083 GM20509Bx01PE20589 3 4 WW \n", + "1425084 GM20509Bx01PE20589 3 5 WW \n", + "1425085 GM20509Bx01PE20589 5 4 WW \n", + "1425086 GM20509Bx01PE20589 0 0 WW \n", + "\n", + "[12886425 rows x 8 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "l_wc = [\n", + " \"/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_ENA_example_full/counts/C7_data/C7_data.txt.gz\",\n", + " \"/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_ENA_example_full/counts/RPE-BM510/RPE-BM510.txt.gz\",\n", + " \"/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_ENA_example_full/counts/RPE1-WT/RPE1-WT.txt.gz\",\n", + " \"/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL/counts/H2NCTAFX2_GM20509B_20s000579-1-1/H2NCTAFX2_GM20509B_20s000579-1-1.txt.gz\"\n", + "]\n", + "\n", + "l_df = pd.concat([pd.read_csv(file, sep=\"\\t\", compression='gzip') for file in l_wc])\n", + "l_df\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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samplecellchromclasscountXX/XY
41C7_data (XX)C7x02PE20301chrXCC1561XX
76C7_data (XX)C7x02PE20302chrXWW1561XX
113C7_data (XX)C7x02PE20303chrXCC1561XX
154C7_data (XX)C7x02PE20304chrXWC1557XX
155C7_data (XX)C7x02PE20304chrXCC4XX
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16526RPE1-WT (XX)RPE1WTPE20492chrXWC1561XX
16558RPE1-WT (XX)RPE1WTPE20493chrXWC1561XX
16596RPE1-WT (XX)RPE1WTPE20494chrXWC1556XX
16597RPE1-WT (XX)RPE1WTPE20494chrXWW5XX
16633RPE1-WT (XX)RPE1WTPE20495chrXWW1561XX
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711 rows Γ— 6 columns

\n", + "
" + ], + "text/plain": [ + " sample cell chrom class count XX/XY\n", + "41 C7_data (XX) C7x02PE20301 chrX CC 1561 XX\n", + "76 C7_data (XX) C7x02PE20302 chrX WW 1561 XX\n", + "113 C7_data (XX) C7x02PE20303 chrX CC 1561 XX\n", + "154 C7_data (XX) C7x02PE20304 chrX WC 1557 XX\n", + "155 C7_data (XX) C7x02PE20304 chrX CC 4 XX\n", + "... ... ... ... ... ... ...\n", + "16526 RPE1-WT (XX) RPE1WTPE20492 chrX WC 1561 XX\n", + "16558 RPE1-WT (XX) RPE1WTPE20493 chrX WC 1561 XX\n", + "16596 RPE1-WT (XX) RPE1WTPE20494 chrX WC 1556 XX\n", + "16597 RPE1-WT (XX) RPE1WTPE20494 chrX WW 5 XX\n", + "16633 RPE1-WT (XX) RPE1WTPE20495 chrX WW 1561 XX\n", + "\n", + "[711 rows x 6 columns]" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats_df = l_df.groupby([\"sample\", \"cell\", \"chrom\"])[\"class\"].value_counts().rename(\"count\").reset_index()\n", + "stats_df_xy = stats_df.loc[(stats_df[\"chrom\"].isin([\"chrX\", \"chrY\"])) ]\n", + "stats_df_xy[\"XX/XY\"] = stats_df_xy[\"sample\"].map(d_xy)\n", + "stats_df_xy[\"sample\"] = stats_df_xy[\"sample\"].replace(\"H2NCTAFX2_GM20509B_20s000579-1-1\", \"LCL\")\n", + "stats_df_xy[\"sample\"] = stats_df_xy[\"sample\"] + \" (\" + stats_df_xy[\"XX/XY\"] + \")\"\n", + "stats_df_xy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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count
countmeanstdmin25%50%75%max
sample
C7_data (XX)248.0969.330645675.1435682.040.01516.01561.01561.0
LCL (XY)96.0764.239583705.9871592.028.0692.01533.01561.0
RPE-BM510 (XX)240.0936.600000690.5185412.038.01429.01561.01561.0
RPE1-WT (XX)127.0983.307087664.1851382.0196.01438.01561.01561.0
\n", + "
" + ], + "text/plain": [ + " count \\\n", + " count mean std min 25% 50% 75% \n", + "sample \n", + "C7_data (XX) 248.0 969.330645 675.143568 2.0 40.0 1516.0 1561.0 \n", + "LCL (XY) 96.0 764.239583 705.987159 2.0 28.0 692.0 1533.0 \n", + "RPE-BM510 (XX) 240.0 936.600000 690.518541 2.0 38.0 1429.0 1561.0 \n", + "RPE1-WT (XX) 127.0 983.307087 664.185138 2.0 196.0 1438.0 1561.0 \n", + "\n", + " \n", + " max \n", + "sample \n", + "C7_data (XX) 1561.0 \n", + "LCL (XY) 1561.0 \n", + "RPE-BM510 (XX) 1561.0 \n", + "RPE1-WT (XX) 1561.0 " + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stats_df_xy.groupby(\"sample\").describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "sns.set_theme()\n", + "plt.rcParams['figure.facecolor'] = 'white'\n", + "plt.rcParams['figure.dpi'] = 150\n", + "#plt.figure(figsize=(30,30))\n", + "g = sns.displot(stats_df_xy.sort_values(by=\"XX/XY\"), x=\"count\", hue=\"sample\", binwidth=10)\n", + "\n", + "\n", + "\n", + "sns.move_legend(\n", + " g, \"upper center\",\n", + " ncol=4, title=None, frameon=False, fontsize=8, bbox_to_anchor=(0.5, 1.05)\n", + ")\n", + "plt.xlabel(\"Ratio\")\n", + "plt.ylabel(\"Density\")\n", + "plt.tight_layout()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0, 0, 'C7_data (XX)'),\n", + " Text(1, 0, 'RPE-BM510 (XX)'),\n", + " Text(2, 0, 'RPE1-WT (XX)'),\n", + " Text(3, 0, 'LCL (XY)')]" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "sns.set_theme()\n", + "plt.rcParams['figure.facecolor'] = 'white'\n", + "plt.rcParams['figure.dpi'] = 150\n", + "#plt.figure(figsize=(30,30))\n", + "g = sns.barplot(data=stats_df_xy.sort_values(by=\"XX/XY\"), x=\"sample\", y=\"count\", hue=\"class\")\n", + "\n", + "sns.move_legend(\n", + " g, \"upper center\",\n", + " ncol=4, title=None, frameon=False, fontsize=8, bbox_to_anchor=(0.5, 1.075)\n", + ")\n", + "plt.xlabel(\"\")\n", + "plt.ylabel(\"W/C # across cells\")\n", + "plt.tight_layout()\n", + "g.set_xticklabels(labels=g.get_xticklabels(), rotation=45)" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "0a6d8766f54173d50f6d2414646668cef466f7ffc6e7b2b5015aca7a502c1dee" + }, + "kernelspec": { + "display_name": "Python 3.7.6 64-bit ('base': conda)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + }, + "orig_nbformat": 4 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/afac/chrxy_analysis.py b/afac/chrxy_analysis.py new file mode 100644 index 00000000..32f4e17e --- /dev/null +++ b/afac/chrxy_analysis.py @@ -0,0 +1,69 @@ +import pandas as pd +import pysam +import os, sys +import subprocess +from io import StringIO +from tqdm import tqdm +import parmap +import multiprocessing as mp + +dirs = [ + "/g/korbel2/weber/MosaiCatcher_files/PRJEB30027/bam/C7_data/selected/", + "/g/korbel2/weber/MosaiCatcher_files/PRJEB30027/bam/RPE1-WT/selected/", + "/g/korbel2/weber/MosaiCatcher_files/PRJEB30027/bam/RPE-BM510/selected/", + "/g/korbel2/weber/MosaiCatcher_files/LCL/H2NCTAFX2_GM20509B_20s000579-1-1/selected/", +] + +for directory_input in dirs: + l_files_selected = [f for f in os.listdir(directory_input) if f.endswith('.bam')] + sample = directory_input.split("/")[-3] + m = mp.Manager() + l_df = m.list() + + # for file in tqdm(l_files_selected): + # print(file) + def loop(file, l_df): + p = subprocess.Popen('samtools idxstats ' + directory_input + file, shell=True, stdout=subprocess.PIPE) + # text = [sub_e.split(" ") for e in p.communicate()[0].decode('utf-8').strip().split('\n') for sub_e in e.lstrip().split('\t')] + text = [e.split('\t') for e in p.communicate()[0].decode('utf-8').strip().split('\n')] + + df = pd.DataFrame.from_records(text, columns=["chr", "chr_length", "read-segments_mapped", "read-segments_unmapped"]) + df = df.loc[df["chr"].isin(["chrX", "chrY"])] + df[["chr_length", "read-segments_mapped", "read-segments_unmapped"]] = df[["chr_length", "read-segments_mapped", "read-segments_unmapped"]].astype(int) + df["Ratio"] = df["read-segments_mapped"] / df["chr_length"] + xy_ratio = df.loc[df["chr"] == "chrX", "Ratio"].values[0] / df.loc[df["chr"] == "chrY", "Ratio"].values[0] + new_df = pd.DataFrame([ + { + "chrX/chrY_ratio" : xy_ratio, + "Cell" : file.replace(".sort.mdup.bam", ""), + "Sample" : sample + }] + ) + l_df.append(new_df) + + + parmap.starmap(loop, list(zip(l_files_selected)), l_df, pm_pbar=True, pm_processes=10) + + final_df = pd.concat(list(l_df)) + final_df.loc[final_df["chrX/chrY_ratio"] >= 5, "M/F"] = "F" + final_df["M/F"] = final_df["M/F"].fillna("M") + + print(final_df) + + mf_dict_from_df = final_df["M/F"].value_counts().to_dict() + mf_dict_from_df.setdefault("M", 0) + mf_dict_from_df.setdefault("F", 0) + print(mf_dict_from_df) + + try: + perc_m = (mf_dict_from_df["M"] - mf_dict_from_df["F"]) / mf_dict_from_df["M"] + perc_f = 1 - perc_m + except: + perc_f = (mf_dict_from_df["F"] - mf_dict_from_df["M"]) / mf_dict_from_df["F"] + perc_m = 1 - perc_f + + print(perc_f, perc_m) + sex = "M" if perc_m > 0.5 else "F" + print(sex) + final_df.to_csv("test_chrxy_analysis{}.tsv".format(sample), sep="\t") + diff --git a/afac/convert_strandphaser_input.R b/afac/convert_strandphaser_input.R new file mode 100644 index 00000000..19d9a1df --- /dev/null +++ b/afac/convert_strandphaser_input.R @@ -0,0 +1,17 @@ +# sink(snakemake@log[[1]]) +library(data.table) +d = fread("/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_HGSVC_correct/segmentation/HTN7CAFXY_HG02492x02_19s003809-1-1/Selection_initial_strand_state") +print(d) +print(unique(d$cell)) +print(unique(d$sample)) +e = fread("/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_HGSVC_correct/counts/HTN7CAFXY_HG02492x02_19s003809-1-1/HTN7CAFXY_HG02492x02_19s003809-1-1.info") +tmp <- basename(e$bam) +e$bam <- strsplit(e$bam, split = ".", fixed = TRUE)[[1]][1] +print(e) +print(unique(e$cell)) +print(unique(e$sample)) +# f <- merge(d, e, by = c("sample", "cell"))[class == "WC", .(chrom, start, end, bam)] +# f <- merge(d, e, by = c("sample", "cell"))[class == "WC", .(chrom, start, end, bam)] +f <- merge(d, e, by = c("sample", "cell")) +print(f) +# write.table(f, file=snakemake@output[[1]], quote=F, row.names=F, col.names=F, sep="\t") \ No newline at end of file diff --git a/afac/handle_selected.py b/afac/handle_selected.py new file mode 100644 index 00000000..c7957bc0 --- /dev/null +++ b/afac/handle_selected.py @@ -0,0 +1,12 @@ +import pandas as pd +import sys, os + +input_dir = sys.argv[1] +input_dir += "/" if input_dir.endswith("/") is False else input_dir + +l_files_all = [f for f in os.listdir(input_dir + "all/") if f.endswith('.bam')] +l_files_selected = [f for f in os.listdir(input_dir + "selected/") if f.endswith('.bam')] +join = list(set(l_files_all).intersection(set(l_files_selected))) +df = pd.DataFrame([{"File" : f} for f in l_files_all]) +df.loc[df["File"].isin(join), "Selected"] = True +df["Selected"] = df["Selected"].fillna(False) diff --git a/afac/haplotag.Table.snakemake.R b/afac/haplotag.Table.snakemake.R new file mode 100644 index 00000000..441b584e --- /dev/null +++ b/afac/haplotag.Table.snakemake.R @@ -0,0 +1,19 @@ + +library(devtools) +load_all("/g/korbel2/weber/Gits/Rsamtools/") +load_all("/g/korbel2/weber/Gits/GenomicAlignments/") + +source("utils/haplotagTable.R") + + +# tab <- getHaplotagTable2( +# bedFile = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG_chr21/haplotag/bed/RPE1-WT/100000.selected_j0.1_s0.5_scedist20.bed", +# bam.file = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG_chr21/haplotag/bam/RPE1-WT/100000.selected_j0.1_s0.5_scedist20/RPE1WTPE20490.sort.mdup.bam", +# file.destination = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG_chr21/test.tsv" +# ) + +tab <- getHaplotagTable2( + bedFile = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/haplotag/bed/RPE1-WT/100000.selected_j0.1_s0.5_scedist20.bed", + bam.file = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/haplotag/bam/RPE1-WT/100000.selected_j0.1_s0.5_scedist20/RPE1WTPE20490.sort.mdup.bam", + file.destination = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/test.tsv" +) \ No newline at end of file diff --git a/afac/haplotagProbs.snakemake.R b/afac/haplotagProbs.snakemake.R new file mode 100644 index 00000000..2a9d8d72 --- /dev/null +++ b/afac/haplotagProbs.snakemake.R @@ -0,0 +1,22 @@ +# log <- file(snakemake@log[[1]], open = "wt") +# sink(file = log, type = "message") +# sink(file = log, type = "output") +args <- commandArgs(trailingOnly = T) + +source("utils/haplotagProbs.R") + +haplotagCounts <- fread(args[1]) +probs <- readRDS(args[2]) + +# FIXME : tmp solution to fix error : Segments must covered all bins, which happen for small scaffolds +chroms <- c("chr1", "chr2", "chr3", "chr4", "chr5", "chr6", "chr7", "chr8", "chr9", "chr10", "chr11", "chr12", "chr13", "chr14", "chr15", "chr16", "chr17", "chr18", "chr19", "chr20", "chr21", "chr22", "chrX") + +haplotagCounts <- haplotagCounts[haplotagCounts$chrom %in% chroms, ] + + +# FIXME: quick and dirty fix for off by one start coordinates of segments +haplotagCounts[, start := start - 1] + +probs <- addHaploCountProbs(probs, haplotagCounts, alpha = 0.05) + +saveRDS(probs, file = args[3]) \ No newline at end of file diff --git a/afac/hgsvc_samples_symlink.py b/afac/hgsvc_samples_symlink.py new file mode 100644 index 00000000..4342e8de --- /dev/null +++ b/afac/hgsvc_samples_symlink.py @@ -0,0 +1,69 @@ +import os + +def list_files(startpath): + for root, dirs, files in os.walk(startpath): + level = root.replace(startpath, '').count(os.sep) + indent = ' ' * 4 * (level) + print('{}{}/'.format(indent, os.path.basename(root))) + subindent = ' ' * 4 * (level + 1) + for f in files: + print('{}{}'.format(subindent, f)) + +import os, sys +from pprint import pprint +from tqdm import tqdm +import shutil + +scratch_dir = "/scratch/jeong/pipeline_mosaicatcher_backup13/pipeline_20190625_HGSVC24_rerun/" + +map_dict = { + "pipeline_20190625_HGSVC24_rerun_GM12329" : "/g/korbel2/StrandSeq/20200109_U24/20191023_GM12329A/bam/", + "pipeline_20190625_HGSVC24_rerun_GM18534" : "/g/korbel2/StrandSeq/20200109_U24/20191120_GM18534B/bam/", + "pipeline_20190625_HGSVC24_rerun_GM18939" : "/g/korbel2/StrandSeq/20200109_U24/20191122_GM18939/bam/", + "pipeline_20190625_HGSVC24_rerun_GM19650" : "/g/korbel2/StrandSeq/20200109_U24/20191120_GM19650A/bam/", + "pipeline_20190625_HGSVC24_rerun_GM19983" : "/g/korbel2/StrandSeq/20200109_U24/20191024_GM19983/bam/", + "pipeline_20190625_HGSVC24_rerun_GM20847" : "/g/korbel2/StrandSeq/20200109_U24/20191203_GM20847B/bam/", + "pipeline_20190625_HGSVC24_rerun_HG00096_HG00171" : "/g/korbel2/StrandSeq/20200109_U24/20191203_HG00096/bam/", + "pipeline_20190625_HGSVC24_rerun_HG00864" : "/g/korbel2/StrandSeq/20200109_U24/20191122_HG00864/bam/", + "pipeline_20190625_HGSVC24_rerun_HG01114" : "/g/korbel2/StrandSeq/20200109_U24/20190911_HG01114/bam/", + "pipeline_20190625_HGSVC24_rerun_HG01505" : "/g/korbel2/StrandSeq/20200109_U24/20191120_HG01505/bam/", + "pipeline_20190625_HGSVC24_rerun_HG01596" : "/g/korbel2/StrandSeq/20200109_U24/20191122_HG01596/bam/", + "pipeline_20190625_HGSVC24_rerun_HG02011" : "/g/korbel2/StrandSeq/20200109_U24/20191023_HG02011/bam/", + "pipeline_20190625_HGSVC24_rerun_HG02492" : "/g/korbel2/StrandSeq/20200109_U24/20200630_HG02492/strand-seq-pipeline/bam/", + "pipeline_20190625_HGSVC24_rerun_HG02587" : "/g/korbel2/StrandSeq/20200109_U24/20191011_HG02587/bam/", + "pipeline_20190625_HGSVC24_rerun_HG02818" : "/g/korbel2/StrandSeq//20200421_RO1_Samples/HG02818/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03009" : "/g/korbel2/StrandSeq/20200109_U24/20191105_HG03009/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03065" : "/g/korbel2/StrandSeq/20200109_U24/20191105_HG03065/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03125" : "/g/korbel2/StrandSeq/20200421_RO1_Samples/20200630_HG03125/strand-seq-pipeline/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03371" : "/g/korbel2/StrandSeq/20200109_U24/20191024_HG03371/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03486" : "/g/korbel2/StrandSeq/20200421_RO1_Samples/20200622_HG03486/strand-seq-pipeline/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03683" : "/g/korbel2/StrandSeq/20200109_U24/20191105_HG03683/bam/", + "pipeline_20190625_HGSVC24_rerun_HG03732" : "/g/korbel2/StrandSeq/20200109_U24/20190917_HG03732/bam/" +} + +final_dir = "/g/korbel2/weber/MosaiCatcher_files/HGSVC_correct/" + + +samples = {d : os.listdir(scratch_dir + d + "/bam/")[-1] for d in os.listdir(scratch_dir)} + +pprint(samples) + +if os.path.exists(final_dir): + shutil.rmtree(final_dir) + +[os.makedirs(final_dir + s + "/all/", exist_ok=True) for p,s in samples.items()] +[os.makedirs(final_dir + s + "/selected/", exist_ok=True) for p,s in samples.items()] + +for p,s in tqdm(samples.items()): + for f in os.listdir(map_dict[p]): + if os.path.islink(final_dir + s + "/all/" + f) is False: + os.symlink(map_dict[p] + f, final_dir + s + "/all/" + f) + + +for d in tqdm(os.listdir(scratch_dir)): + for f in os.listdir(scratch_dir + d + "/bam/" + samples[d] + "/selected/"): + # print(final_dir + samples[d] + "/selected/" + f) + os.symlink(final_dir + samples[d] + "/all/" + f, final_dir + samples[d] + "/selected/" + f) + + +# list_files(final_dir) \ No newline at end of file diff --git a/afac/lite_mosaic_count.py b/afac/lite_mosaic_count.py new file mode 100644 index 00000000..63b87a22 --- /dev/null +++ b/afac/lite_mosaic_count.py @@ -0,0 +1,15 @@ +import pandas as pd +import sys + +df_counts = pd.read_csv(sys.argv[1], compression="gzip", sep="\t") +sample_cells = df_counts["cell"].unique().tolist()[:2] +df_counts_lite = df_counts.loc[df_counts["cell"].isin(sample_cells)] +df_counts_lite.to_csv(sys.argv[3], sep="\t", compression="gzip", index=False) + +info_header = "".join([line for line in open(sys.argv[2], "r").readlines() if line[0] == "#"]) +with open(sys.argv[4], "w") as w: + w.write(info_header) + +df_info = pd.read_csv(sys.argv[2], sep="\t", skiprows=13) +df_info_lite = df_info.loc[df_info["cell"].isin(sample_cells)] +df_info_lite.to_csv(sys.argv[4], sep="\t", mode="a", index=False) diff --git a/afac/load_rdata.R b/afac/load_rdata.R new file mode 100644 index 00000000..e69de29b diff --git a/afac/mosaiClassifier.snakemake.R b/afac/mosaiClassifier.snakemake.R new file mode 100644 index 00000000..1d5af368 --- /dev/null +++ b/afac/mosaiClassifier.snakemake.R @@ -0,0 +1,34 @@ +# sink(snakemake@log[[1]]) +library(data.table) +library(assertthat) +source("utils/mosaiClassifier/mosaiClassifier.R") + + +# Currently read files from the Snakemake pipeline +counts <- fread(paste("zcat", "/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/counts/H2NCTAFX2_GM20509B_20s000579-1-1/H2NCTAFX2_GM20509B_20s000579-1-1.txt.gz")) +info <- fread("/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/counts/H2NCTAFX2_GM20509B_20s000579-1-1/H2NCTAFX2_GM20509B_20s000579-1-1.info") +strand <- fread("/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/strandphaser/H2NCTAFX2_GM20509B_20s000579-1-1/StrandPhaseR_final_output.txt") +segs <- fread("/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_singularity_LCL-TALL/segmentation/H2NCTAFX2_GM20509B_20s000579-1-1/Selection_jointseg.txt") + +# chroms <- c("chr1", "chr2") + +# counts <- counts[counts$chrom %in% chroms, ] +# strand <- strand[strand$chrom %in% chroms, ] +# segs <- segs[segs$chrom %in% chroms, ] + +print(counts) +print(strand) +print(segs) + +# haplotypeMode? +if ("CW" %in% strand$class) { + haplotypeMode <- T +} else { + haplotypeMode <- F +} + +d <- mosaiClassifierPrepare(counts, info, strand, segs) +print(d) +e <- mosaiClassifierCalcProbs(d, maximumCN = 4, haplotypeMode = haplotypeMode) + +saveRDS(e, file = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/sv_probabilities/RPE1-WT/100000.selected_j0.1_s0.5_scedist20/probabilities.Rdata") \ No newline at end of file diff --git a/afac/mosaiClassifier_call.snakemake.R b/afac/mosaiClassifier_call.snakemake.R new file mode 100644 index 00000000..40b0bf25 --- /dev/null +++ b/afac/mosaiClassifier_call.snakemake.R @@ -0,0 +1,26 @@ +library(data.table) +source("/g/korbel2/weber/workspace/mosaicatcher-update/workflow/scripts/mosaiclassifier_scripts/mosaiClassifier/makeSVcalls.R") + +probs <- readRDS("/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG_dryrun/mosaiclassifier/haplotag_likelihoods/RPE-BM510.Rdata") +llr <- as.numeric(4) +use.pop.priors <- eval(parse(text = TRUE)) +use.haplotags <- eval(parse(text = TRUE)) +regularizationFactor <- 10^(-as.numeric(6)) +genotype.cutoff <- as.numeric(0) +minFrac.used.bins <- as.numeric(0.8) +bin.size <- as.numeric(100000) + +print(probs) + +print(llr) + +probs <- mosaiClassifierPostProcessing(probs, regularizationFactor = regularizationFactor) + +print(probs) + +tab <- makeSVCallSimple(probs, llr_thr = llr, use.pop.priors = use.pop.priors, use.haplotags = use.haplotags, genotype.cutoff = genotype.cutoff, bin.size, minFrac.used.bins = minFrac.used.bins) + +print(tab) + + +# write.table(tab, file = , sep = "\t", quote = F, row.names = F, col.names = T) \ No newline at end of file diff --git a/afac/plot-clustering.snakemake.R b/afac/plot-clustering.snakemake.R new file mode 100644 index 00000000..5857f906 --- /dev/null +++ b/afac/plot-clustering.snakemake.R @@ -0,0 +1,7 @@ +source("utils/plot-clustering.R") +plot.clustering( + inputfile = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/sv_calls/RPE1-WT/100000.selected_j0.1_s0.5_scedist20/simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE.txt", + bin.bed.filename = "utils/bin_200kb_all.bed", + position.outputfile = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/sv_calls/RPE-BM510/100000.selected_j0.1_s0.5_scedist20/plots/sv_clustering/simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE-position.pdf", + chromosome.outputfile = "/g/korbel2/weber/MosaiCatcher_output/MosaiCatcher_output_sample_KG/sv_calls/RPE-BM510/100000.selected_j0.1_s0.5_scedist20/plots/sv_clustering/simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE-chromosome.pdf" +) \ No newline at end of file diff --git a/afac/prepare_segments.r b/afac/prepare_segments.r new file mode 100644 index 00000000..4d0ff7c8 --- /dev/null +++ b/afac/prepare_segments.r @@ -0,0 +1,13 @@ +library(data.table) +qu = 0.4 +print(qu) +d = fread("/g/korbel2/weber/MosaiCatcher_output/segmentation/ERR2940607/100000.txt") +print(d) +print('\n') +print(1:max(70)) +print(quantile(1:max(70), qu, type = 1)) +stop() +# type = 1 is important to get discrete values! +d = d[, .SD[k == quantile(1:max(k), qu, type = 1)], by=chrom][,.(k, chrom, bps)] +print(d) +# write.table(d, file = snakemake@output[[1]], row.names=F, quote=F, sep="\t") \ No newline at end of file diff --git a/afac/test_ggplot.r b/afac/test_ggplot.r new file mode 100644 index 00000000..85c011c7 --- /dev/null +++ b/afac/test_ggplot.r @@ -0,0 +1,23 @@ +options(scipen = 999) # turn-off scientific notation like 1e+48 +library(ggplot2) +theme_set(theme_bw()) # pre-set the bw theme. +data("midwest", package = "ggplot2") +# midwest <- read.csv("http://goo.gl/G1K41K") # bkup data source + +print(midwest) + +# Scatterplot +gg <- ggplot(midwest, aes(x = area, y = poptotal)) + + geom_point(aes(col = state, size = popdensity)) + + geom_smooth(method = "loess", se = F) + + xlim(c(0, 0.1)) + + ylim(c(0, 500000)) + + labs( + subtitle = "Area Vs Population", + y = "Population", + x = "Area", + title = "Scatterplot", + caption = "Source: midwest" + ) + +plot(gg) \ No newline at end of file diff --git a/afac/test_pypdf.py b/afac/test_pypdf.py new file mode 100644 index 00000000..e670754e --- /dev/null +++ b/afac/test_pypdf.py @@ -0,0 +1,11 @@ +from PyPDF2 import PdfFileWriter, PdfFileReader +import os, sys + +inputpdf = PdfFileReader(open(sys.argv[1], "rb")) +output_pdf, file_extension = os.path.splitext(sys.argv[2]) +for i in range(inputpdf.numPages): + inputpdf = PdfFileReader(open(sys.argv[1], "rb")) + output = PdfFileWriter() + output.addPage(inputpdf.getPage(i)) + with open(output_pdf + "-{}".format(i) + file_extension, "wb") as outputStream: + output.write(outputStream) diff --git a/afac/test_pysam.py b/afac/test_pysam.py new file mode 100644 index 00000000..0ba5c79a --- /dev/null +++ b/afac/test_pysam.py @@ -0,0 +1,27 @@ +import pysam +import sys, os +import pandas as pd + +bam_file_path = "/g/korbel2/weber/MosaiCatcher_files/bam_KG_full/RPE1-WT/all/RPE1WTPE20401.sort.mdup.bam" +# bam_file_path = sys.argv[1] + + +# def check_bam_header(bam_file_path): +# # GET BAM FILE HEADER WITH PYSAM +# h = pysam.view("-H", bam_file_path) +# h = [e.split("\t") for e in h.split("\n")] +# sm_tag_list = list(set([sub_e.replace("SM:", "") for e in h for sub_e in e if "SM:" in sub_e])) + +# # FOLDER NAME BASED ON PATH +# folder_name = bam_file_path.split("/")[-3] + +# # CHECKS +# assert len(sm_tag_list) == 1, "Two different SM tags in the header of BAM file {}".format(bam_file_path) +# assert sm_tag_list[0] == folder_name, "SM tag in BAM file {} do not correspond to folder name {}".format(bam_file_path, folder_name) + + +# check_bam_header(bam_file_path=bam_file_path) + + + +print(df_h) diff --git a/afac/test_readgaligmentspairs.R b/afac/test_readgaligmentspairs.R new file mode 100644 index 00000000..015d5d38 --- /dev/null +++ b/afac/test_readgaligmentspairs.R @@ -0,0 +1,14 @@ +library(devtools) + +load_all("/g/korbel2/weber/Gits/Rsamtools/") +load_all("/g/korbel2/weber/Gits/GenomicAlignments/") +load_all("/g/korbel2/weber/Gits/StrandPhaseR/") + +bamfile <- "/g/korbel2/weber/MosaiCatcher_files/tmp/h/RPE1WTPE20401.sort.mdup.bam" +bamindex <- "/g/korbel2/weber/MosaiCatcher_files/tmp/h/RPE1WTPE20401.sort.mdup.bam.bai" +region <- vcf2ranges("/g/korbel2/weber/MosaiCatcher_output_sample_KG_chr21_test/snv_genotyping/RPE1-WT/chr21.vcf") + +readGAlignmentPairs(bamfile, index = bamindex, param = Rsamtools::ScanBamParam( + tag = "XA", which = + range(region), what = c("seq", "qual", "mapq", "cigar", "flag"), +)) \ No newline at end of file diff --git a/afac/tmp_input_smk.py b/afac/tmp_input_smk.py new file mode 100644 index 00000000..f39ee3b4 --- /dev/null +++ b/afac/tmp_input_smk.py @@ -0,0 +1,78 @@ +import math +from collections import defaultdict + +configfile: "config/Snake.config_embl.yaml" +import pandas as pd +import os, sys +from pprint import pprint +import pysam +from tqdm import tqdm + +# TODO I/O : Function to define inputs ; simplify list/dict system // SOLVED +# TODO Use remote file system to download example files + + +def handle_input_data(thisdir, exclude_list=list): + """ """ + # Parsing folder and retrieve only files with .bam extension + data = [(r, file.replace(".bam", "")) for r, d, f in os.walk(thisdir) for file in f if ".bam" in file and ".bai" not in file] + + # Building pandas df based on folder structure + df = pd.DataFrame(data, columns=["Folder", "File"]) + + # Defining cols + df["all/selected"] = df["Folder"].apply(lambda r: r.split("/")[-1]) + df["Sample"] = df["Folder"].apply(lambda r: r.split("/")[-2]) + df["Cell"] = df["File"].apply(lambda r: r.split(".")[0]) + df["Full_path"] = df["Folder"] + "/" + df["File"] + ".bam" + + # Filtering based on exclude list defined + df_config_files = df.loc[~df["Cell"].isin(exclude_list)] + + # Export dicts + SAMPLES = sorted(df_config_files.Sample.unique().tolist()) + BAM_PER_SAMPLE = df_config_files.loc[df_config_files["all/selected"] == "selected"].groupby("Sample")["File"].apply(list).to_dict() + CELL_PER_SAMPLE = df_config_files.loc[df_config_files["all/selected"] == "selected"].groupby("Sample")["Cell"].apply(list).to_dict() + ALLBAMS_PER_SAMPLE = df_config_files.loc[df_config_files["all/selected"] == "all"].groupby("Sample")["File"].apply(list).to_dict() + + return SAMPLES, BAM_PER_SAMPLE, CELL_PER_SAMPLE, ALLBAMS_PER_SAMPLE, df_config_files + + +def check_bam_header(bam_file_path): + """ """ + + # Get BAM file header with pysam + h = pysam.view("-H", bam_file_path) + h = [e.split("\t") for e in h.split("\n")] + sm_tag_list = list(set([sub_e.replace("SM:", "") for e in h for sub_e in e if "SM:" in sub_e])) + + # Folder name based on path + folder_name = bam_file_path.split("/")[-3] + + # Assertions + assert len(sm_tag_list) == 1, "Two different SM tags in the header of BAM file {}".format(bam_file_path) + assert sm_tag_list[0] == folder_name, 'Folder name "{}" must correspond to SM tag in BAM file "{}"'.format(folder_name, bam_file_path) + + +# FIXME : tmp solution to remove bad cells => need to fix this with combination of ASHLEYS ? +# TODO : other solution by giving in config file, CLI input ? + +exclude_list = ["BM510x3PE20490"] + +SAMPLES, BAM_PER_SAMPLE, CELL_PER_SAMPLE, ALLBAMS_PER_SAMPLE, df_config_files = handle_input_data( + thisdir="/g/korbel2/weber/MosaiCatcher_files/bam_KG_full/", exclude_list=exclude_list +) + +# print(df_config_files) +# print(df_config_files["Full_path"][0]) + +tqdm.pandas(desc="Checking if BAM SM tags correspond to folder names") +df_config_files["Full_path"].progress_apply( + check_bam_header, +) + +all_dict = df_config_files.loc[df_config_files["all/selected"] == "all"].groupby("Sample")["Cell"].nunique().to_dict() +selected_dict = df_config_files.loc[df_config_files["all/selected"] == "selected"].groupby("Sample")["Cell"].nunique().to_dict() +print("Detected {} samples:".format(df_config_files.Sample.nunique())) +[print(" {}:\t{} cells\t {} selected cells".format(s, all_dict[s], selected_dict[s])) for s in SAMPLES] + diff --git a/afac/update_tag.sh b/afac/update_tag.sh new file mode 100644 index 00000000..95513352 --- /dev/null +++ b/afac/update_tag.sh @@ -0,0 +1,8 @@ +#!/bin/bash + +OLD_TAG="$1" && + NEW_TAG=$(echo "$OLD_TAG" | sed 's/v//g') && + echo "$OLD_TAG" "$NEW_TAG" && + git tag "$NEW_TAG" "$OLD_TAG" && + git tag -d "$OLD_TAG" && + git push $2 "$NEW_TAG" :"$OLD_TAG" diff --git a/config/config.yaml b/config/config.yaml new file mode 100644 index 00000000..f4a4c6d6 --- /dev/null +++ b/config/config.yaml @@ -0,0 +1,100 @@ +####################################### +# MOSAICATCHER CONFIGURATION FILE. # +####################################### + +mail: "" + +####################################### +## Command-line options +####################################### + +### Modes ["count", "segmentation", "mosaiclassifier"] +mode: "mosaiclassifier" +### Plot enabled [True] or disabled [False] +plot: False +### Enable / Disable comparison for each BAM file between folder name & SM tag +check_sm_tag: True +### Enable / Disable download of BAM examples (RPE-BM510) +dl_bam_example: False +### Enable / Disable download of external files (1000G SNV & Fasta ref genome) +dl_external_files: False + +## Input BAM location +input_bam_location: "TEST_EXAMPLE_DATA/" +## Output location +output_location: "TEST_OUTPUT/" + +# External files +## 1000G SNV sites to genotype : https://sandbox.zenodo.org/record/1060653/files/ALL.chr1-22plusX_GRCh38_sites.20170504.renamedCHR.vcf.gz +snv_sites_to_genotype: "workflow/sandbox.zenodo.org/record/1074721/files/ALL.chr1-22plusX_GRCh38_sites.20170504.renamedCHR.vcf.gz" + +# Reference genome : https://sandbox.zenodo.org/record/1060653/files/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna +reference: "workflow/sandbox.zenodo.org/record/1074721/files/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna" + +# Chromosomes list to process +chromosomes: + - chr21 + +############################## +# Advanced configuration. # +############################## + +# Enable conda & singularity usage +use-conda: True +use-singularity: True +singularity-args: "-B /g:/g" +latency-wait: 60 + +# Default conda frontend +conda-frontend: "mamba" + +# Print shell commands +printshellcmds: True + +# External static data +# R Ref +R_reference: "BSgenome.Hsapiens.UCSC.hg38" +# SegDups file +segdups: "workflow/data/segdups/segDups_hg38_UCSCtrack.bed.gz" + +# Exclude cells +exclude_list: [] + +# Strandphaser +git_commit_strandphaser: "69c9fb4" +git_repo_strandphaser: "https://github.com/daewoooo/StrandPhaseR" +paired_end: True + +# CHECKME : Parameters optimization ? +# PARAMETERS + +## WINDOW +window: 100000 + +## METHODS +methods: + - "simpleCalls_llr4_poppriorsTRUE_haplotagsTRUE_gtcutoff0_regfactor6_filterFALSE" + - "simpleCalls_llr4_poppriorsTRUE_haplotagsFALSE_gtcutoff0.05_regfactor6_filterTRUE" + +llr: 4 +poppriors: TRUE +haplotags: + - TRUE + - FALSE +gtcutoff: + - 0 + - 0.05 +regfactor: 6 +filter: + - TRUE + - FALSE + +## BPDENS +### JOINT SEG +min_diff_jointseg: 0.1 +### SINGLE SEG +min_diff_singleseg: 0.5 +### SCE CUTOFF +additional_sce_cutoff: 20000000 +### SCE MIN DISTANCE +sce_min_distance: 500000 diff --git a/docs/Input.md b/docs/Input.md new file mode 100644 index 00000000..e69de29b diff --git a/docs/Output.md b/docs/Output.md new file mode 100644 index 00000000..3396bb05 --- /dev/null +++ b/docs/Output.md @@ -0,0 +1,11 @@ +# Outputs + +## Mosaic count - reads density across bins + +## SV calls + +## SV consistency + +## SV clustering + +## Statistics diff --git a/docs/Parameters.md b/docs/Parameters.md new file mode 100644 index 00000000..2348062f --- /dev/null +++ b/docs/Parameters.md @@ -0,0 +1,116 @@ + +# Parameters + +## MosaiCatcher arguments + + + + +``` +mode +``` + +MosaiCatcher currently supports three different modes of execution : `count`, `segmentation` and `mosaiclassifier`. +- `count` (selected by default) will only performs `Mosaic count` binning and count reads for each bin produced +- `segmentation` will run the pipeline until the `Mosaic segmentation` and selection of the correct segments +- `mosaiclassifier` will run the complete pipeline until the detection of SV in each selected cell of the samples + +To select your mode of execution, use the following argument `--config mode=[count|segmentation|mosaiclassifier]` + + +``` +plot +``` + +For each of these modes, you can *enable* or *disable* the plots generation by using `--config plot=[True|False]` + + +``` +check_sm_tag +``` +Based on pysam, will compare for each BAM file, if the header SM tag is identical to the folder name in order to prevent further issues. + +``` +dl_bam_example +``` +Allow to retrieve automatically BAM example data to run the pipeline. + +``` +dl_external_files +``` +Allow to retrieve automatically external files (GRCh38 reference genome + 1000G SNV VCF file) required to run the pipeline. + + + +## Snakemake arguments + +Here are presented some essential snakemake options that could help you. + +``` +--cores, -c +``` +Use at most N CPU cores/jobs in parallel. If N is omitted or β€˜all’, the limit is set to the number of available CPU cores. In case of cluster/cloud execution, this argument sets the number of total cores used over all jobs (made available to rules via workflow.cores). + +``` +--printshellcmds, -p +``` +Recommended to print out the shell commands that will be executed. + +``` +--use-conda +``` +If defined in the rule, run job in a conda environment. If this flag is not set, the conda directive is ignored and use the current environment (and path system) to execute the command. + +``` +--conda-frontend [mamba|conda] +``` +Choose the conda frontend for installing environments. Mamba is much faster and highly recommended. Default: β€œmamba” + + +``` +--use-singularity +``` +If defined in the rule, run job within a singularity container. If this flag is not set, the singularity directive is ignored. + +``` +--singularity-args "-B /mounting_point:/mounting_point" +``` +Pass additional args to singularity. `-B` stands for binding point between the host and the container. + +``` +--dryrun, -n +``` +Do not execute anything, and display what would be done. If you have a very large workflow, use –dry-run –quiet to just print a summary of the DAG of jobs. + +``` +--rerun-incomplete, --ri +``` +Re-run all jobs the output of which is recognized as incomplete. + + +``` +--keep-going, -k +``` +Go on with independent jobs if a job fails. + +``` +-T, --retries, --restart-times +``` +Number of times to restart failing jobs (defaults to 0). + +``` +--forceall, -F +``` +Force the execution of the selected (or the first) rule and all rules it is dependent on regardless of already created output. + +--- +**ℹ️ Note** + +Currently, the binding command needs to correspond to the mounting point of your system (i.e: "/tmp:/tmp"). +On seneca for example (EMBL), use `"/g:/g"` if you are working on `/g/korbel[2]` or `"/scratch:/scratch"` if you plan to work on `scratch`. + +--- + +Obviously, all other [snakemake CLI options](https://snakemake.readthedocs.io/en/stable/executing/cli.html) can also be used. + + diff --git a/docs/Usage.md b/docs/Usage.md new file mode 100644 index 00000000..10625bd1 --- /dev/null +++ b/docs/Usage.md @@ -0,0 +1,268 @@ + +# Usage + +## Quick Start + +1. Install [Singularity](https://www.sylabs.io/guides/3.0/user-guide/) +2. Create a dedicated conda environment +``` +conda create -n mosaicatcher_env -c conda-forge -c bioconda snakemake pandas pysam imagemagick tqdm && conda activate mosaicatcher_env +``` +3. Clone the repository and go in the `workflow` directory +``` +git clone https://git.embl.de/tweber/mosaicatcher-update.git && cd mosaicatcher-update/workflow/ +``` +4. Download test and reference data +``` +snakemake -c1 --config mode=download_data dl_external_files=True dl_bam_example=True input_bam_location=TEST_EXAMPLE_DATA/ +``` +5. Run on test data +``` +snakemake --cores 12 input_bam_location=TEST_EXAMPLE_DATA/ output_location=TEST_OUTPUT/ --use-conda --use-singularity --singularity-args "-B /:/" +``` + +6. Start running your own analysis +``` +snakemake --cores 12 --config input_bam_location= output_location= --use-conda --use-singularity --singularity-args "-B /:/" + +``` +7. Generate report +``` +snakemake --cores 12 --config input_bam_location= output_location= --report +``` + +## System requirements + +This workflow is meant to be run in a Unix-based operating system (tested on Ubuntu 18.04 & CentOS 7). + +Minimum system requirements vary based on the use case. We highly recommend running it in a server environment with 32+GB RAM and 12+ cores. + + +- [Conda install instructions](https://conda.io/miniconda.html) +- [Singularity install instructions](https://sylabs.io/guides/3.0/user-guide/quick_start.html#quick-installation-steps) + +## Detailed usage + +### 🐍 1. Mosaicatcher basic conda environment install + +MosaiCatcher leverages snakemake built-in features such as execution within container and conda predefined modular environments. That's why it is only necessary to create an environment that relies on [snakemake](https://github.com/snakemake/snakemake) (to execute the pipeline) and [pandas](https://github.com/pandas-dev/pandas) (to handle basic configuration). If you plan to generate HTML Web report including plots, it is also necessary to install [imagemagick](https://github.com/ImageMagick/ImageMagick). Finally, [pysam](https://pysam.readthedocs.io/en/latest/api.html) and [tqdm](https://github.com/tqdm/tqdm) are currently required to enable `check_sm_tag` feature that compare BAM SM tag to folder name. + + +If possible, it is also highly recommended to install and use `mamba` package manager instead of `conda`, which is much more efficient. + +``` +conda install -c conda-forge mamba +mamba create -n mosaicatcher_env -c conda-forge -c bioconda snakemake pandas pysam imagemagick +conda activate mosaicatcher_env +``` + + + +### ‡️ 2. Clone repository & go into workflow directory + +After cloning the repo, go into the `workflow` directory which correspond to the pipeline entry point. + + + + +``` +git clone https://github.com/friendsofstrandseq/mosaicatcher-pipeline.git +cd mosaicatcher-pipeline/workflow/ +``` + + +### βš™οΈ 3. MosaiCatcher config and execution + +MosaiCatcher takes different arguments to run. Default configuration (`worfklow/config/config.yaml`) looks like the following. + +```yaml +# Required arguments + +## Modes ["count", "segmentation", "mosaiclassifier"] +mode: "count" +## Plot enabled [True] or disabled [False] +plot: False +## Enable / Disable comparison for each BAM file between folder name & SM tag +check_sm_tag: True +## Enable / Disable download of BAM examples (RPE-BM510) +dl_bam_example: False +## Enable / Disable download of external files (1000G SNV & Fasta ref genome) +dl_external_files: False +## Input BAM location +input_bam_location: "TEST_EXAMPLE_DATA/" +## Output location +output_location: "TEST_OUTPUT/" + +# External files +## 1000G SNV sites to genotype : https://sandbox.zenodo.org/record/1060653/files/ALL.chr1-22plusX_GRCh38_sites.20170504.renamedCHR.vcf.gz +snv_sites_to_genotype: "sandbox.zenodo.org/record/1062182/files/ALL.chr1-22plusX_GRCh38_sites.20170504.renamedCHR.vcf.gz" +# Reference genome : https://sandbox.zenodo.org/record/1060653/files/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna +reference: "sandbox.zenodo.org/record/1062182/files/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna" + +# Chromosomes list to process +chromosomes: [chr1, chr2, chr3, chr4, chr5, chr6, chr7, chr8, chr9, chr10, chr11, chr12, chr13, chr14, chr15, chr16, chr17, chr18, chr19, chr20, chr21, chr22, chrX] +``` + +You can either change it or override YAML file by using snakemake CLI arguments as the following : + +``` +--config mode=segmentation plot=False input_bam_location=/HELLO_WORLD output_location=/AU_REVOIR +``` + +The `--config` argument will here overrides value of each of the keys present in the YAML file. + + + + +#### 3A. Download external data automatically with snakemake [Optional] + +``` +snakemake -c1 --config mode=download_data dl_external_files=True +``` + +#### 3B. Strand-Seq BAM input data + +##### (i) Download example data automatically with snakemake [Optional] + +``` +snakemake -c1 --config mode=download_data dl_bam_example=True input_bam_location=TEST_EXAMPLE_DATA/ +``` +**Warning:** Download example data currently requires 3GB of free space disk. + + +##### (ii) Use your own data + +In its current flavour, MosaiCatcher requires that input data must be formatted the following way : + + +```bash +Parent_folder +|-- Sample_1 +| |-- all +| | |-- Cell_01.sort.mdup.bam +| | |-- Cell_02.sort.mdup.bam +| | |-- Cell_03.sort.mdup.bam +| | `-- Cell_04.sort.mdup.bam +| `-- selected +| |-- Cell_01.sort.mdup.bam +| |-- Cell_02.sort.mdup.bam +| `-- Cell_04.sort.mdup.bam +`-- Sample_2 + |-- all + | |-- Cell_21.sort.mdup.bam + | |-- Cell_22.sort.mdup.bam + | |-- Cell_23.sort.mdup.bam + | `-- Cell_24.sort.mdup.bam + `-- selected + |-- Cell_22.sort.mdup.bam + |-- Cell_23.sort.mdup.bam + `-- Cell_24.sort.mdup.bam +``` + +In a `Parent_Folder`, create a subdirectory `Parent_Folder/sampleName/` for each `sample`. Your Strand-seq BAM files of this sample go into two folders: + +* `all/` for the total set of BAM files +* `selected/` for the subset of successful Strand-seq libraries (possibly hard-linked to `all/`) + +It is important to follow these rules for single-cell data + +* One BAM file per cell +* Sorted and indexed + * If BAM files are not indexed, please use a writable folder in order that the pipeline generate itself the index `.bai` files +* Timestamp of index files must be newer than of the BAM files +* Each BAM file must contain a read group (`@RG`) with a common sample name (`SM`), which must match the folder name (`sampleName` above) + + + +### ⚑️ 4. Run the pipeline + +#### Local execution (without batch scheduler) + +After defining your configuration, you can launch the pipeline the following way if you downloaded BAM example data using 3A: + + +```bash +snakemake \ + --cores 20 \ + --config \ + plot=True \ + mode=mosaiclassifier \ + --use-conda --use-singularity --singularity-args "-B /:/" +``` + +Otherwise, you must specify your input and output folder like the following: + +```bash +snakemake \ + --cores 20 \ + --config \ + plot=True \ + mode=mosaiclassifier \ + output_location=OUTPUT_FOLDER \ + input_bam_location=INPUT_FOLDER \ + --use-conda --use-singularity --singularity-args "-B /:/" +``` + +--- +**ℹ️ Note** + +It is recommended to first run the command and check if there are any anomalies with the `--dry-run` option + +--- + +--- +**⚠️ Warning** + +If you are experiencing any issues with conda-frontend snakemake option, please use `--conda-frontend conda` instead of `mamba` + +--- + +#### HPC execution + +MosaiCatcher can be executed on HPC using [Slurm](https://slurm.schedmd.com/documentation.html) by leveraging snakemake profile feature. Current Slurm profile [`workflow/profiles/slurm/config.yaml`] was defined and tested on EMBL HPC cluster but can be modified, especially regarding **partition** setting. + +##### Current strategy to solve HPC job OOM + +Workflow HPC execution usually needs to deal with out of memory (OOM) errors, out of disk space, abnormal paths or missing parameters for the scheduler. To deal with OOM, we are currently using snakemake restart feature (thanks [@Pablo Moreno](https://github.com/pcm32)) in order to automatically double allocated memory to the job at each attempt (limited to 8 for the moment). Then, if a job fails to run with the default 1GB of memory allocated, it will be automatically restarted tith 2GB at the 2nd attempt, 4GB at the 3rd, etc. + +To execute MosaiCatcher on HPC, use the following command. + +##### Command + +```bash +snakemake \ + --profile profiles/slurm/ \ + --config \ + plot=True \ + mode=mosaiclassifier \ + output_location=OUTPUT_FOLDER \ + input_bam_location=INPUT_FOLDER \ + --use-conda --use-singularity --singularity-args "-B /:/" +``` + +The `logs` and `errors` directory will be automatically created in the current directory, corresponding respectively to the `output` and `error` parameter of the `sbatch` command. + + +### πŸ“Š 5. Generate report [Optional] + +Optionally, you can also MosaiCatcher rules that produce plots + +```bash +snakemake \ + --cores 20 \ + --config \ + plot=True \ + mode=mosaiclassifier \ + output_location=OUTPUT_FOLDER \ + input_bam_location=INPUT_FOLDER \ + --report report.zip +``` + + + +--- +**ℹ️ Note** + +The zip file produced can be heavy (~1GB for 24 HGSVC samples ; 2000 cells) if multiple samples are processed in parallel in the same output folder. + +--- \ No newline at end of file diff --git a/docs/images/mosaic_logo.png b/docs/images/mosaic_logo.png new file mode 100644 index 00000000..96fd70c2 Binary files /dev/null and b/docs/images/mosaic_logo.png differ diff --git a/github-actions-runner/Dockerfile b/github-actions-runner/Dockerfile new file mode 100644 index 00000000..6108ccb2 --- /dev/null +++ b/github-actions-runner/Dockerfile @@ -0,0 +1,104 @@ +FROM docker.io/weber8thomas/mosaicatcher-pipeline:1.3 +# FROM condaforge/mambaforge:latest + + +# LABEL io.github.snakemake.containerized="true" +# LABEL io.github.snakemake.conda_env_hash="a3ef2d63f463ab104533db0acccdb49051f093890112a1e15d67ceec1c2012a2" +# +# # Step 1: Retrieve conda environments +# +# # Conda environment: +# # source: workflow/envs/mc_base.yaml +# # prefix: /conda-envs/cd15c940fe1181e1751e02b25c7fcfb7 +# # name: mc-base +# # channels: +# # - conda-forge +# # - bioconda +# # dependencies: +# # - pandas +# # - pysam +# # - tqdm +# # - perl +# # - pypdf2 +# # - parmap +# RUN mkdir -p /conda-envs/cd15c940fe1181e1751e02b25c7fcfb7 +# COPY workflow/envs/mc_base.yaml /conda-envs/cd15c940fe1181e1751e02b25c7fcfb7/environment.yaml +# +# # Conda environment: +# # source: workflow/envs/mc_bioinfo_tools.yaml +# # prefix: /conda-envs/491a340c3c20c63397962b44091da5b0 +# # name: mc-bioinfo-tools +# # channels: +# # - conda-forge +# # - bioconda +# # dependencies: +# # - samtools +# # - bcftools +# # - tabix +# # - freebayes +# # - bcftools +# # - whatshap +# RUN mkdir -p /conda-envs/491a340c3c20c63397962b44091da5b0 +# COPY workflow/envs/mc_bioinfo_tools.yaml /conda-envs/491a340c3c20c63397962b44091da5b0/environment.yaml +# +# # Conda environment: +# # source: workflow/envs/rtools.yaml +# # prefix: /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33 +# # name: rtools +# # channels: +# # - conda-forge +# # - bioconda +# # - r +# # dependencies: +# # - bioconductor-biocparallel=1.16.6 +# # - bioconductor-bsgenome +# # - bioconductor-bsgenome.hsapiens.ucsc.hg19 +# # - bioconductor-bsgenome.hsapiens.ucsc.hg38=1.4.1 +# # - bioconductor-fastseg=1.28.0 +# # - bioconductor-genomicalignments=1.18.1 +# # - bioconductor-genomicranges=1.34.0 +# # - bioconductor-rsamtools=1.34.0 +# # - bioconductor-s4vectors=0.20.1 +# # - r-assertthat=0.2.1 +# # - r-base=3.5.1 +# # - r-biocmanager +# # - r-cowplot=1.0.0 +# # - r-data.table=1.12.6 +# # - r-devtools=2.2.2 +# # - r-doparallel +# # - r-foreach +# # - r-ggplot2=3.3.0 +# # - r-gtools=3.8.1 +# # - r-reshape2=1.4.3 +# # - r-scales=1.1.0 +# # - r-zoo=1.8_3 +# # - r-dplyr=0.8.5 +# # - r-mc2d=0.1_18 +# # - r-pheatmap=1.0.12 +# # - bioconductor-complexheatmap=2.0.0 +# # - r-gplots=3.0.3 +# # - r-scales=1.1.0 +# # - r-rcolorbrewer=1.1_2 +# # - r-stringr=1.4.0 +# RUN mkdir -p /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33 +# COPY workflow/envs/rtools.yaml /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33/environment.yaml +# +# # Step 2: Generate conda environments +# +# RUN mamba env create --prefix /conda-envs/cd15c940fe1181e1751e02b25c7fcfb7 --file /conda-envs/cd15c940fe1181e1751e02b25c7fcfb7/environment.yaml && \ +# mamba env create --prefix /conda-envs/491a340c3c20c63397962b44091da5b0 --file /conda-envs/491a340c3c20c63397962b44091da5b0/environment.yaml && \ +# mamba env create --prefix /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33 --file /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33/environment.yaml && \ +# mamba clean --all -y +# +# ---------------- +# CUSTOM PART +# TAG 1.3 +# ---------------- +# ---------------- + +# RUN mamba install -c conda-forge -c bioconda samtools && \ +# chmod -R 0777 /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33/lib/R/library && \ +# pwd && \ +# apt-get install git && \ +# git clone -b smk_workflow_catalog https://github.com/friendsofstrandseq/mosaicatcher-pipeline.git /mosaicatcher-pipeline && \ +# /conda-envs/7bb3be0079f74fda9f2f7d6337d01c33/bin/Rscript /mosaicatcher-pipeline/workflow/scripts/strandphaser_scripts/install_strandphaser.R 69c9fb4 https://github.com/daewoooo/StrandPhaseR diff --git a/workflow/envs/cpp.yaml b/workflow/envs/cpp.yaml new file mode 100644 index 00000000..9379d25f --- /dev/null +++ b/workflow/envs/cpp.yaml @@ -0,0 +1,10 @@ +name: cpp +channels: + - conda-forge + - bioconda +dependencies: + - make=4.3=h516909a_0 + - cmake=3.16.4=h28c56e5_0 + - boost-cpp=1.72.0=h8e57a91_0 + - htslib=1.10.2=h78d89cc_0 + - pkg-config=0.29.2=h516909a_1006 diff --git a/workflow/envs/cpp_copy.yaml b/workflow/envs/cpp_copy.yaml new file mode 100644 index 00000000..826c21c8 --- /dev/null +++ b/workflow/envs/cpp_copy.yaml @@ -0,0 +1,50 @@ +name: cpp_copy +channels: + - conda-forge + - bioconda +dependencies: + - binutils_impl_linux-64 + - binutils_linux-64 + - boost-cpp + - ca-certificates + - cxx-compiler + - cmake + - gcc + - gcc_impl_linux-64 + - gcc_linux-64 + - gsl + - gxx_impl_linux-64 + - gxx_linux-64 + - htslib + - ld_impl_linux-64 + - libgcc-ng + - libgomp + - libiconv + - libidn2 + - libopenblas + - libpng + - libprotobuf + - librsvg + - libssh2 + - libstdcxx-ng + - libtiff + - libtool + - libunistring + - libuuid + - libuv + - libwebp + - libxcb + - libxml2 + - llvm-openmp + - lz4-c + - make + - ncurses + - openssl + - pkg-config + - pthread-stubs + - readline + - rhash + - wget + - xz + - zlib + - zstd diff --git a/workflow/envs/mc_base.yaml b/workflow/envs/mc_base.yaml new file mode 100644 index 00000000..d5886667 --- /dev/null +++ b/workflow/envs/mc_base.yaml @@ -0,0 +1,11 @@ +name: mc-base +channels: + - conda-forge + - bioconda +dependencies: + - pandas + - pysam + - tqdm + - perl + - pypdf2 + - parmap diff --git a/workflow/envs/mc_bioinfo_tools.yaml b/workflow/envs/mc_bioinfo_tools.yaml new file mode 100644 index 00000000..a2c6be4b --- /dev/null +++ b/workflow/envs/mc_bioinfo_tools.yaml @@ -0,0 +1,11 @@ +name: mc-bioinfo-tools +channels: + - conda-forge + - bioconda +dependencies: + - samtools + - bcftools + - tabix + - freebayes + - bcftools + - whatshap diff --git a/workflow/envs/rtools.yaml b/workflow/envs/rtools.yaml new file mode 100644 index 00000000..d2d8caec --- /dev/null +++ b/workflow/envs/rtools.yaml @@ -0,0 +1,36 @@ +name: rtools +channels: + - conda-forge + - bioconda + - r +dependencies: + - bioconductor-biocparallel=1.16.6 + - bioconductor-bsgenome + - bioconductor-bsgenome.hsapiens.ucsc.hg19 + - bioconductor-bsgenome.hsapiens.ucsc.hg38=1.4.1 + - bioconductor-fastseg=1.28.0 + - bioconductor-genomicalignments=1.18.1 + - bioconductor-genomicranges=1.34.0 + - bioconductor-rsamtools=1.34.0 + - bioconductor-s4vectors=0.20.1 + - r-assertthat=0.2.1 + - r-base=3.5.1 + - r-biocmanager + - r-cowplot=1.0.0 + - r-data.table=1.12.6 + - r-devtools=2.2.2 + - r-doparallel + - r-foreach + - r-ggplot2=3.3.0 + - r-gtools=3.8.1 + - r-reshape2=1.4.3 + - r-scales=1.1.0 + - r-zoo=1.8_3 + - r-dplyr=0.8.5 + - r-mc2d=0.1_18 + - r-pheatmap=1.0.12 + - bioconductor-complexheatmap=2.0.0 + - r-gplots=3.0.3 + - r-scales=1.1.0 + - r-rcolorbrewer=1.1_2 + - r-stringr=1.4.0 diff --git a/workflow/profiles/slurm/config.yaml b/workflow/profiles/slurm/config.yaml new file mode 100644 index 00000000..48eb6255 --- /dev/null +++ b/workflow/profiles/slurm/config.yaml @@ -0,0 +1,31 @@ +# Cluster command +cluster: mkdir -p logs/{rule} errors/{rule} && + sbatch + --partition={resources.partition} + --qos={resources.qos} + --cpus-per-task={threads} + --mem={resources.mem_mb} + --job-name=smk-{rule}-{wildcards} + --output=logs/{rule}/%j.out + --error=errors/{rule}/%j.error + --time={resources.time} + +# Default ressources for all rules +default-resources: + - partition=htc + - qos=normal + - mem_mb=1000 + - time=30 + +max-jobs-per-second: 10 +max-status-checks-per-second: 1 +local-cores: 1 +latency-wait: 60 +jobs: 500 + +# 8 attempts to restart to correspond to the progressive memory increase solving OOM +restart-times: 8 +keep-going: True +rerun-incomplete: True + +scheduler: greedy diff --git a/workflow/report/custom-stylesheet.css b/workflow/report/custom-stylesheet.css new file mode 100644 index 00000000..864556cc --- /dev/null +++ b/workflow/report/custom-stylesheet.css @@ -0,0 +1,9 @@ +#brand { + margin: auto; + height: 60px; + width: 100%; + background-repeat: no-repeat; + background-size: 50%; + background-position: center; + background-image: url(../../docs/mosaic_logo.png) +} \ No newline at end of file diff --git a/workflow/report/mosaic_counts.rst b/workflow/report/mosaic_counts.rst new file mode 100644 index 00000000..7d341a03 --- /dev/null +++ b/workflow/report/mosaic_counts.rst @@ -0,0 +1 @@ +Mosaic Count plots for sample {{ snakemake.wildcards.sample }} and cell {{ snakemake.wildcards.cell }}. diff --git a/workflow/report/strandphaser_haplotypes.rst b/workflow/report/strandphaser_haplotypes.rst new file mode 100644 index 00000000..52ddf902 --- /dev/null +++ b/workflow/report/strandphaser_haplotypes.rst @@ -0,0 +1 @@ +StrandPhaseR haplotype plots for sample {{ snakemake.wildcards.sample }} and chromosome {{ snakemake.wildcards.chrom }}. diff --git a/workflow/report/workflow.rst b/workflow/report/workflow.rst new file mode 100644 index 00000000..b9339e93 --- /dev/null +++ b/workflow/report/workflow.rst @@ -0,0 +1,8 @@ +The Snakemake workflow management system is a tool to create **reproducible and scalable** data analyses. +Workflows are described via a human readable, Python based language. +They can be seamlessly scaled to server, cluster, grid and cloud environments, without the need to modify the workflow definition. +Finally, Snakemake workflows can entail a description of required software, which will be automatically deployed to any execution environment. + +Snakemake is **highly popular**, with `>7 new citations per week `_. +For an introduction, please visit https://snakemake.github.io. + diff --git a/workflow/rules/count.smk b/workflow/rules/count.smk new file mode 100644 index 00000000..baf34b17 --- /dev/null +++ b/workflow/rules/count.smk @@ -0,0 +1,144 @@ +from workflow.scripts.utils.utils import get_mem_mb + +import pandas as pd +config_df = pd.read_csv(config["output_location"] + "config/config_df.tsv", sep="\t") + +pd.options.display.max_colwidth = 40 +bam_per_sample_local = config_df.loc[config_df["Selected"] == True].groupby("Sample")["File"].apply(list).to_dict() + +################################################################################ +# Read counting # +################################################################################ + + +# DOCME : mosaic count read orientation ? +rule mosaic_count: + """ + rule fct: Call mosaic count C++ function to count reads in each BAM file according defined window + input: For the moment, individual BAM file in the selected folder of the associated sample + output: counts: read counts for the BAM file according defined window ; info file : summary statistics + """ + input: + bam = lambda wc: expand(config["input_bam_location"] + wc.sample + "/selected/{bam}.bam", bam = bam_per_sample_local[str(wc.sample)] if wc.sample in bam_per_sample_local else "FOOBAR"), + # bai = lambda wc: expand(config["input_bam_location"] + wc.sample + "/selected/{bam}.bam.bai", bam = bam_per_sample_local[wc.sample]) if wc.sample in bam_per_sample_local else "FOOBAR", + excl = ancient(config["output_location"] + "config/exclude_file"), + output: + counts = config["output_location"] + "counts/{sample}/{sample}.txt.fixme.gz", + info = config["output_location"] + "counts/{sample}/{sample}.info" + log: + config["output_location"] + "log/counts/{sample}/mosaic_count.log" + container: + "library://weber8thomas/remote-build/mosaic:0.3" + params: + window = config["window"] + resources: + mem_mb = get_mem_mb, + shell: + """ + /mosaicatcher/build/mosaic count \ + --verbose \ + --do-not-blacklist-hmm \ + -o {output.counts} \ + -i {output.info} \ + -x {input.excl} \ + -w {params.window} \ + {input.bam} \ + > {log} 2>&1 + """ + + +rule order_mosaic_count_output: + input: + config["output_location"] + "counts/{sample}/{sample}.txt.fixme.gz" + output: + config["output_location"] + "counts/{sample}/{sample}.txt.gz" + run: + df = pd.read_csv(input[0], compression='gzip', sep='\t') + df = df.sort_values(by=["sample", "cell", "chrom", "start"]) + df.to_csv(output[0], index=False, compression="gzip", sep="\t") + + +# CHECKME : to keep or to improve ? @jeong @mc @kg +################################################################################ +# Normalize counts # +################################################################################ + +# TODO : check if inversion file is corresponded to previously published +rule merge_blacklist_bins: + """ + rule fct: Call Python script to merge HGVSC normalization defined file & inversion whitelist file + input: norm: HGSVC predefined BED file by the group ; whitelist: whitelist inversion file predefined by the group + """ + input: + norm = "utils/normalization/HGSVC.{window}.txt", + whitelist = "utils/normalization/inversion-whitelist.tsv", + output: + merged = config["output_location"] + "normalizations/HGSVC.{window}.merged.tsv" + log: + config["output_location"] + "log/merge_blacklist_bins/{window}.log" + + shell: + """ + PYTHONPATH="" # Issue #1031 (https://bitbucket.org/snakemake/snakemake/issues/1031) + utils/merge-blacklist.py --merge_distance 500000 {input.norm} --whitelist {input.whitelist} --min_whitelist_interval_size 100000 > {output.merged} 2>> {log} + """ + +# FIXME : snakemake ambiguity with I/O paths +# CHECKME : Check R code for normalization +rule normalize_counts: + """ + rule fct: Normalization of mosaic counts based on merged normalization file produced with a linear relation (count * scaling_factor) + input: counts: counts file coming from `rule mosaic_count` ; norm: merged normalization file produced by `rule merge_blacklist_bins` + output: normalized counts based predefined factors for each window + """ + input: + counts = config["output_location"] + "counts/{sample}/{window}.txt.gz", + norm = config["output_location"] + "normalizations/HGSVC.{window}.merged.tsv", + output: + config["output_location"] + "norm_counts/{sample}/{window}.txt.gz" + log: + config["output_location"] + "log/normalize_counts/{sample}/{window}.log" + shell: + """ + Rscript utils/normalize.R {input.counts} {input.norm} {output} 2>&1 > {log} + """ + +# FIXME : cleaner way to symlink info files +rule link_normalized_info_file: + """ + rule fct: Symlink info file ouput mosaic count to normalization count directory + input: Global summary statistics produced by mosaic count + output: symlink in norm_counts output directory + """ + input: + info = config["output_location"] + "counts/{sample}/{window}.info" + output: + info = config["output_location"] + "norm_counts/{sample}/{window}.info" + run: + d = os.path.dirname(output.info) + file = os.path.basename(output.info) + shell("cd {d} && ln -s {input.info} {file}") + + + +################################################################################ +# Single-Cell Segmentation # +################################################################################ + +rule extract_single_cell_counts: + """ + rule fct: extract from count the rows coming from the given cell + input: mosaic count output file for the sample according a given window + output: count per cell file for the sample according a given window + """ + input: + config["output_location"] + "counts/{sample}/{sample}.txt.gz" + output: + config["output_location"] + "counts/{sample}/counts-per-cell/{cell}.txt.gz" + shell: + """ + # Issue #1022 (https://bitbucket.org/snakemake/snakemake/issues/1022) + zcat {input} | awk -v name={wildcards.cell} '(NR==1) || $5 == name' | gzip > {output} + """ + + diff --git a/workflow/rules/examples.smk b/workflow/rules/examples.smk new file mode 100644 index 00000000..a790ff09 --- /dev/null +++ b/workflow/rules/examples.smk @@ -0,0 +1,46 @@ +import os +from snakemake.remote.HTTP import RemoteProvider as HTTPRemoteProvider + +HTTP = HTTPRemoteProvider() + +rule dl_example_data: + """ + rule fct: Download BAM example data as input for MosaiCatcher pipeline + input: zip file stored on Zenodo + output: input_bam_location given by the user + """ + input: + HTTP.remote("https://sandbox.zenodo.org/record/1074721/files/TEST_EXAMPLE_DATA.zip", keep_local=True) + # HTTP.remote("https://sandbox.zenodo.org/record/1062186/files/report_TALL.zip", keep_local=True) + output: + # directory(config["input_bam_location"]) + touch(config["output_location"] + "config/dl_example_data.ok") + shell: + "unzip {input} -d ." + + +# TODO: Adapt according reference +rule dl_external_data: + """ + rule fct: Download External files + input: files stored on Zenodo + output: touch file to check if everything was running correctly + """ + input: + HTTP.remote("https://sandbox.zenodo.org/record/1074721/files/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna", keep_local=True), + HTTP.remote("https://sandbox.zenodo.org/record/1074721/files/ALL.chr1-22plusX_GRCh38_sites.20170504.renamedCHR.vcf.gz", keep_local=True), + output: + touch(config["output_location"] + "config/dl_external_data.ok") + +# TODO: Adapt according reference +rule dl_external_data_index: + """ + rule fct: Download External files + input: files stored on Zenodo + output: touch file to check if everything was running correctly + """ + input: + HTTP.remote("https://sandbox.zenodo.org/record/1074721/files/GCA_000001405.15_GRCh38_no_alt_analysis_set.fna.fai", keep_local=True), + HTTP.remote("https://sandbox.zenodo.org/record/1074721/files/ALL.chr1-22plusX_GRCh38_sites.20170504.renamedCHR.vcf.gz.tbi", keep_local=True), + output: + touch(config["output_location"] + "config/dl_external_data_index.ok") diff --git a/workflow/rules/haplotagging.smk b/workflow/rules/haplotagging.smk new file mode 100644 index 00000000..4b14523d --- /dev/null +++ b/workflow/rules/haplotagging.smk @@ -0,0 +1,68 @@ +from workflow.scripts.utils.utils import get_mem_mb + +import pandas as pd +config_df = pd.read_csv(config["output_location"] + "config/config_df.tsv", sep="\t") +bam_per_sample = config_df.loc[config_df["Selected"] == True].groupby("Sample")["File"].apply(list).to_dict() + +################################################################################ +# Haplotagging # +################################################################################ + + +rule haplotag_bams: + input: + vcf = config["output_location"] + "strandphaser/phased-snvs/{sample}.vcf.gz", + tbi = config["output_location"] + "strandphaser/phased-snvs/{sample}.vcf.gz.tbi", + bam = config["input_bam_location"] + "{sample}/selected/{cell}.bam", + # bai = config["input_bam_location"] + "{sample}/selected/{cell}.bam.bai" + output: + config["output_location"] + "haplotag/bam/{sample}/{cell}.bam", + log: + config["output_location"] + "log/haplotag_bams/{sample}/{cell}.log" + params: + ref = config["reference"] + resources: + mem_mb = get_mem_mb, + conda: + "../envs/mc_bioinfo_tools.yaml" + shell: + # DOCME : --skip-missing-contigs option to remove unused chroms + # "whatshap haplotag -o {output} -r {params.ref} {input.vcf} {input.bam} > {log} 2>{log} " + "whatshap haplotag --skip-missing-contigs -o {output} -r {params.ref} {input.vcf} {input.bam} > {log} 2>{log} " + +rule create_haplotag_segment_bed: + input: + segments = config["output_location"] + "segmentation/{sample}/Selection_jointseg.txt", + output: + bed = config["output_location"] + "haplotag/bed/{sample}.bed", + shell: + """ + # Issue #1022 (https://bitbucket.org/snakemake/snakemake/issues/1022) + awk -v s={config[window]} -f workflow/scripts/haplotagging_scripts/create_haplotag_segment_bed.awk {input.segments} > {output.bed} + """ + +rule create_haplotag_table: + input: + bam = config["output_location"] + "haplotag/bam/{sample}/{cell}.bam", + bai = config["output_location"] + "haplotag/bam/{sample}/{cell}.bam.bai", + bed = config["output_location"] + "haplotag/bed/{sample}.bed" + output: + tsv = config["output_location"] + "haplotag/table/{sample}/by-cell/{cell}.tsv" + log: + config["output_location"] + "log/create_haplotag_table/{sample}.{cell}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/haplotagging_scripts/haplotagTable.snakemake.R" + +rule merge_haplotag_tables: + input: + tsvs = lambda wc: [config["output_location"] + "haplotag/table/{}/by-cell/{}.tsv".format(wc.sample, cell) for cell in bam_per_sample[wc.sample]], + output: + tsv = config["output_location"] + "haplotag/table/{sample}/haplotag_counts_merged.tsv" + shell: + "(head -n1 {input.tsvs[0]} && tail -q -n +2 {input.tsvs}) > {output.tsv}" + + diff --git a/workflow/rules/input_check.smk b/workflow/rules/input_check.smk new file mode 100644 index 00000000..9c4a28b4 --- /dev/null +++ b/workflow/rules/input_check.smk @@ -0,0 +1,21 @@ +# Pipeline input rules + +rule generate_exclude_file_for_mosaic_count: + """ + rule fct: + input: + output: + """ + input: + ancient(config["output_location"] + "config/config_df.tsv"), + bam = config["input_bam_location"] + output: + config["output_location"] + "config/exclude_file" + params: + chroms = config["chromosomes"] + conda: + "../envs/mc_base.yaml" + script: + "../scripts/utils/generate_exclude_file.py" + + diff --git a/workflow/rules/mosaiclassifier.smk b/workflow/rules/mosaiclassifier.smk new file mode 100644 index 00000000..6e631c62 --- /dev/null +++ b/workflow/rules/mosaiclassifier.smk @@ -0,0 +1,92 @@ +from workflow.scripts.utils.utils import get_mem_mb + +################################################################################ +# MosaiClassifier # +################################################################################ + +rule mosaiClassifier_calc_probs: + input: + counts = config["output_location"] + "counts/{sample}/{sample}.txt.gz", + info = config["output_location"] + "counts/{sample}/{sample}.info", + + states = config["output_location"] + "strandphaser/{sample}/StrandPhaseR_final_output.txt", + bp = config["output_location"] + "segmentation/{sample}/Selection_jointseg.txt" + output: + output = config["output_location"] + "mosaiclassifier/sv_probabilities/{sample}/probabilities.Rdata" + log: + config["output_location"] + "log/mosaiClassifier_calc_probs/{sample}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/mosaiclassifier_scripts/mosaiClassifier.snakemake.R" + +rule create_haplotag_likelihoods: + input: + haplotag_table = config["output_location"] + "haplotag/table/{sample}/haplotag_counts_merged.tsv", + sv_probs_table = config["output_location"] + "mosaiclassifier/sv_probabilities/{sample}/probabilities.Rdata", + output: + config["output_location"] + 'mosaiclassifier/haplotag_likelihoods/{sample}.Rdata' + log: + config["output_location"] + "log/create_haplotag_likelihoods/{sample}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/mosaiclassifier_scripts/haplotagProbs.snakemake.R" + + +rule mosaiClassifier_make_call: + input: + probs = config["output_location"] + 'mosaiclassifier/haplotag_likelihoods/{sample}.Rdata' + output: + config["output_location"] + "mosaiclassifier/sv_calls/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}_filterFALSE.tsv" + log: + config["output_location"] + "log/mosaiClassifier_make_call/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.log" + conda: + "../envs/rtools.yaml" + params: + minFrac_used_bins = 0.8, + window = config["window"] + resources: + mem_mb = get_mem_mb, + script: + "../scripts/mosaiclassifier_scripts/mosaiClassifier_call.snakemake.R" + + +rule mosaiClassifier_make_call_biallelic: + input: + probs = config["output_location"] + "sv_probabilities/{sample}/probabilities.Rdata" + output: + config["output_location"] + "sv_calls/{sample}/biAllelic_llr{llr}.txt" + log: + config["output_location"] + "log/mosaiClassifier_make_call_biallelic/{sample}/{llr}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/mosaiclassifier_scripts/mosaiClassifier_call_biallelic.snakemake.R" + + +rule call_complex_regions: + input: + calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}.tsv", + output: + complex_regions = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}.complex.tsv", + log: + config["output_location"] + "log/call_complex_regions/{sample}/{method}.log" + conda: + "../envs/mc_base.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + PYTHONPATH="" # Issue #1031 (https://bitbucket.org/snakemake/snakemake/issues/1031) + workflow/scripts/mosaiclassifier_scripts/call-complex-regions.py \ + --merge_distance 5000000 \ + --ignore_haplotypes \ + --min_cell_count 2 {input.calls} > {output.complex_regions} 2>{log} + """ diff --git a/workflow/rules/plots.smk b/workflow/rules/plots.smk new file mode 100644 index 00000000..23b2dbad --- /dev/null +++ b/workflow/rules/plots.smk @@ -0,0 +1,144 @@ +from workflow.scripts.utils.utils import get_mem_mb + +import os +os.environ['OPENBLAS_NUM_THREADS'] = '1' + +# TODO : option for segmentation : enable/disable back colors + +################################################################################ +# Plots # +################################################################################ + +# Load rules only if plot is enabled [True] in config file +if config["plot"] is True: + + rule plot_mosaic_counts: + """ + rule fct: Plot function of read counts for each bam file + input: mosaic count outputs (counts & info) + output: Generate figure based on couting results + """ + input: + counts = config["output_location"] + "counts/{sample}/{sample}.txt.gz", + info = config["output_location"] + "counts/{sample}/{sample}.info" + output: + config["output_location"] + "plots/{sample}/counts/CountComplete.pdf" + log: + config["output_location"] + "log/plot_mosaic_counts/{sample}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + Rscript workflow/scripts/plotting/qc.R {input.counts} {input.info} {output} > {log} 2>&1 + """ + # TODO : from shell to script function + + rule divide_pdf: + input: + config["output_location"] + "plots/{sample}/counts/CountComplete.pdf" + output: + report( + config["output_location"] + "plots/{sample}/counts/{cell}.{i, \d+}.pdf", + caption="../report/mosaic_counts.rst", + category="Mosaic counts", + subcategory = "{sample}", + labels={"Cell" : "{cell}", "Nb" : "{i}"} + ) + conda: + "../envs/mc_base.yaml" + params: + config_df = config["output_location"] + "config/config_df.tsv" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/plotting/dividing_pdf.py" + + + rule plot_SV_consistency_barplot: + input: + sv_calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}.tsv", + output: + barplot_bypos = report(config["output_location"] + "plots/{sample}/sv_consistency/{method}.consistency-barplot-bypos.pdf", category="SV Consistency", subcategory="{sample}", labels={"Barplot type" : "By position", "method" : "{method}", }), + barplot_byaf = report(config["output_location"] + "plots/{sample}/sv_consistency/{method}.consistency-barplot-byaf.pdf", category="SV Consistency", subcategory="{sample}", labels={"Barplot type" : "By AF", "method" : "{method}", }), + log: + config["output_location"] + "log/plot_SV_consistency/{sample}/{method}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/plotting/sv_consistency_barplot.snakemake.R" + + + rule plot_clustering: + input: + sv_calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}.tsv", + # binbed = "data/bin_200kb_all.bed", + binbed = "workflow/data/bin_200kb_all.bed", + output: + position = report(config["output_location"] + "plots/{sample}/sv_clustering/{method}-position.pdf", category="SV Clustering", subcategory="{sample}", labels={"method" : "{method}", }), + chromosome = config["output_location"] + "plots/{sample}/sv_clustering/{method}-chromosome.pdf", + # chromosome = report(config["output_location"] + "plots/{sample}/sv_clustering/{method}-chromosome.pdf", category="SV clustering"), + log: + config["output_location"] + "log/plot_clustering/{sample}/{method}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/plotting/plot-clustering.snakemake.R" + # Rscript scripts/plotting/plot-clustering.snakemake.R {input.sv_calls} {input.binbed} {output.position} {output.chromosome} + + rule plot_SV_calls: + input: + counts = config["output_location"] + "counts/{sample}/{sample}.txt.gz", + calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}_filter{filter}.tsv", + complex_calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}_filter{filter}.complex.tsv", + strand = config["output_location"] + "strandphaser/{sample}/StrandPhaseR_final_output.txt", + segments = config["output_location"] + "segmentation/{sample}/Selection_jointseg.txt", + scsegments = config["output_location"] + "segmentation/{sample}/Selection_singleseg.txt", + grouptrack = config["output_location"] + "mosaiclassifier/postprocessing/group-table/{sample}/{method}.tsv", + output: + # config["output_location"] + "plots/{sample}/sv_calls/{method}_filter{filter}.{chrom}.pdf" + report(config["output_location"] + "plots/{sample}/sv_calls/{method}_filter{filter}.{chrom}.pdf", category="SV Calls", subcategory="{sample}", labels={"method" : "{method}", "filter" : "{filter}", "Chrom" : "{chrom}"}) + log: + config["output_location"] + "log/plot_SV_calls/{sample}/{method}_filter{filter}.{chrom}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + Rscript workflow/scripts/plotting/plot-sv-calls.R \ + segments={input.segments} \ + singlecellsegments={input.scsegments} \ + strand={input.strand} \ + complex={input.complex_calls} \ + groups={input.grouptrack} \ + calls={input.calls} \ + {input.counts} \ + {wildcards.chrom} \ + {output} > {log} 2>&1 + """ + + # TODO : rule merge_plot_SV_calls ? + + + # TODO : from shell to script function + + + + # rule generate_halo_json: + # input: + # counts = config["output_location"] + "counts/{sample}/{windows}.txt.gz", + # output: + # json = config["output_location"] + "halo/{sample}/{windows}.json.gz", + # log: + # config["output_location"] + "log/generate_halo_json/{sample}/{windows}.{windows}.log" + # shell: + # """ + # PYTHONPATH="" # Issue #1031 (https://bitbucket.org/snakemake/snakemake/issues/1031) + # (./utils/counts_to_json.py {input.counts} | gzip > {output.json}) + # """ diff --git a/workflow/rules/postprocessing.smk b/workflow/rules/postprocessing.smk new file mode 100644 index 00000000..a5314fa9 --- /dev/null +++ b/workflow/rules/postprocessing.smk @@ -0,0 +1,76 @@ +from workflow.scripts.utils.utils import get_mem_mb +################################################################################ +# PostProcessing # +################################################################################ + + +# CHECKME : segdup file only in hg38 +rule postprocessing_filter: + input: + calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}_filterFALSE.tsv" + output: + calls = config["output_location"] + "mosaiclassifier/postprocessing/filter/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.tsv" + conda: + "../envs/mc_base.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + export LC_CTYPE=en_US.UTF-8 + export LC_ALL=en_US.UTF-8 + workflow/scripts/postprocessing/filter_MosaiCatcher_calls.pl {input.calls} {config[segdups]} > {output.calls} + """ + +rule postprocessing_merge: + input: + calls = config["output_location"] + "mosaiclassifier/postprocessing/filter/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.tsv" + output: + calls = config["output_location"] + "mosaiclassifier/postprocessing/merge/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.tsv" + conda: + "../envs/mc_base.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + export LC_CTYPE=en_US.UTF-8 + export LC_ALL=en_US.UTF-8 + workflow/scripts/postprocessing/group_nearby_calls_of_same_AF_and_generate_output_table.pl {input.calls} > {output.calls} + """ + + +rule postprocessing_sv_group_table: + input: + calls = config["output_location"] + "mosaiclassifier/postprocessing/merge/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.tsv" + output: + grouptrack = config["output_location"] + "mosaiclassifier/postprocessing/group-table/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.tsv" + conda: + "../envs/mc_base.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + PYTHONPATH="" # Issue #1031 (https://bitbucket.org/snakemake/snakemake/issues/1031) + workflow/scripts/postprocessing/create-sv-group-track.py {input.calls} > {output.grouptrack} + """ + + + +rule filter_calls: + input: + # inputcalls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}_filterFALSE.tsv", + inputcalls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}_filterFALSE.tsv", + # mergedcalls = config["output_location"] + "mosaiclassifier/postprocessing/merge/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}.tsv", + mergedcalls = config["output_location"] + "mosaiclassifier/postprocessing/merge/{sample}/{method}.tsv", + output: + # calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/simpleCalls_llr{llr}_poppriors{pop_priors}_haplotags{use_haplotags}_gtcutoff{gtcutoff}_regfactor{regfactor}_filterTRUE.tsv" + calls = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}_filterTRUE.tsv" + conda: + "../envs/mc_base.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + PYTHONPATH="" # Issue #1031 (https://bitbucket.org/snakemake/snakemake/issues/1031) + workflow/scripts/postprocessing/apply_filter.py {input.inputcalls} {input.mergedcalls} > {output.calls} + """ + diff --git a/workflow/rules/regenotyping.smk b/workflow/rules/regenotyping.smk new file mode 100644 index 00000000..99a4d062 --- /dev/null +++ b/workflow/rules/regenotyping.smk @@ -0,0 +1,71 @@ +from workflow.scripts.utils.utils import get_mem_mb + +import pandas as pd +config_df = pd.read_csv(config["output_location"] + "config/config_df.tsv", sep="\t") +allbams_per_sample = config_df.groupby("Sample")["File"].apply(list).to_dict() + + +################################################################################ +# REGENOTYPE SNV # +################################################################################ + +rule mergeBams: + """ + rule fct: + input: + output: + """ + input: + lambda wc: expand(config["input_bam_location"] + wc.sample + "/all/{bam}.bam", bam = allbams_per_sample[wc.sample]) if wc.sample in allbams_per_sample else "FOOBAR", + output: + config["output_location"] + "merged_bam/{sample}/merged.bam" + log: + config["output_location"] + "log/mergeBams/{sample}.log" + resources: + mem_mb = get_mem_mb, + time = "01:00:00", + threads: + 10 + conda: + "../envs/mc_bioinfo_tools.yaml" + shell: + "samtools" + " merge -@ {threads} {output} {input} 2>&1 > {log}" + +rule regenotype_SNVs: + """ + rule fct: + input: + output: + """ + input: + bam = config["output_location"] + "merged_bam/{sample}/merged.bam", + bai = config["output_location"] + "merged_bam/{sample}/merged.bam.bai", + sites = config["snv_sites_to_genotype"], + output: + vcf = config["output_location"] + "snv_genotyping/{sample}/{chrom,chr[0-9A-Z]+}.vcf" + log: + config["output_location"] + "log/snv_genotyping/{sample}/{chrom}.log" + params: + fa = config["reference"], + resources: + mem_mb = get_mem_mb, + time = "10:00:00", + conda: + "../envs/mc_bioinfo_tools.yaml" + shell: + """ + (freebayes \ + -f {params.fa} \ + -r {wildcards.chrom} \ + -@ {input.sites} \ + --only-use-input-alleles {input.bam} \ + --genotype-qualities \ + | bcftools view \ + --exclude-uncalled \ + --genotype het \ + --types snps \ + --include "QUAL>=10" \ + > {output.vcf}) 2> {log} + """ + + diff --git a/workflow/rules/segmentation.smk b/workflow/rules/segmentation.smk new file mode 100644 index 00000000..b46e9532 --- /dev/null +++ b/workflow/rules/segmentation.smk @@ -0,0 +1,136 @@ +from workflow.scripts.utils.utils import get_mem_mb + +import math +import pandas as pd +config_df = pd.read_csv(config["output_location"] + "config/config_df.tsv", sep="\t") +cell_per_sample = config_df.loc[config_df["Selected"] == True].groupby("Sample")["Cell"].apply(list).to_dict() + +################################################################################ +# Joint Segmentation # +################################################################################ + + +# CHECKME : @Marco mention on Gitlab +# CHECKME : parameters +rule segmentation: + """ + rule fct: Identify breakpoints of futur SV based on normalized read counts + input: mosaic [normalized] counts + output: Segmentation tab file + """ + input: + config["output_location"] + "counts/{sample}/{sample}.txt.gz" + output: + config["output_location"] + "segmentation/{sample}/{sample}.txt.fixme" + log: + config["output_location"] + "log/segmentation/{sample}/{sample}.log" + params: + min_num_segs = lambda wc: math.ceil(200000 / float(config["window"])) # bins to represent 200 kb + container: + "library://weber8thomas/remote-build/mosaic:0.3" + resources: + mem_mb = get_mem_mb, + shell: + """ + /mosaicatcher/build/mosaic segment \ + --remove-none \ + --forbid-small-segments {params.min_num_segs} \ + -M 50000000 \ + -o {output} \ + {input} > {log} 2>&1 + """ + +# FIXME: This is a workaround because latest versions of "mosaic segment" don't compute the "bps" column properly. Remove once fixed in the C++ code. +rule fix_segmentation: + """ + rule fct: + input: + output: + """ + input: + config["output_location"] + "segmentation/{sample}/{sample}.txt.fixme" + output: + config["output_location"] + "segmentation/{sample}/{sample}.txt" + shell: + """ + # Issue #1022 (https://bitbucket.org/snakemake/snakemake/issues/1022) + awk -v name={wildcards.sample} -v window={config[window]} -f workflow/scripts/segmentation_scripts/fix_segmentation.awk {input} > {output} + """ + + +################################################################################ +# Single-Cell Segmentation # +################################################################################ + +rule segment_one_cell: + """ + rule fct: Same as `rule segmentation` : mosaic segment function but for individual cell + input: mosaic count splitted by cell produced by `rule extract_single_cell_counts` + output: Segmentation file for an individual cell + """ + input: + config["output_location"] + "counts/{sample}/counts-per-cell/{cell}.txt.gz" + output: + config["output_location"] + "segmentation/{sample}/segmentation-per-cell/{cell}.txt" + log: + config["output_location"] + "log/segmentation/{sample}/segmentation-per-cell/{cell}.log" + container: + "library://weber8thomas/remote-build/mosaic:0.3" + params: + # mc_command = config["mosaicatcher"], + min_num_segs = lambda wc: math.ceil(200000 / float(config["window"])) # bins to represent 200 kb + resources: + mem_mb = get_mem_mb, + shell: + """ + /mosaicatcher/build/mosaic segment \ + --remove-none \ + --forbid-small-segments {params.min_num_segs} \ + -M 50000000 \ + -o {output} \ + {input} > {log} 2>&1 + """ + + +rule segmentation_selection: + """ + rule fct: + input: mosaic read counts (txt.gz) & stats info (.info) + joint & sc segmentation + output: initial_strand_state used for the following by strandphaser + """ + input: + counts=config["output_location"] + "counts/{sample}/{sample}.txt.gz", + jointseg=config["output_location"] + "segmentation/{sample}/{sample}.txt", + singleseg=lambda wc: [config["output_location"] + "segmentation/{}/segmentation-per-cell/{}.txt".format(wc.sample, cell) for cell in cell_per_sample[wc.sample]], + info=config["output_location"] + "counts/{sample}/{sample}.info", + output: + jointseg=config["output_location"] + "segmentation/{sample}/Selection_jointseg.txt", + singleseg=config["output_location"] + "segmentation/{sample}/Selection_singleseg.txt", + strand_states=config["output_location"] + "segmentation/{sample}/Selection_initial_strand_state", + log: + config["output_location"] + "log/segmentation/segmentation_selection/{sample}.log" + params: + cellnames = lambda wc: ",".join(cell for cell in cell_per_sample[wc.sample]), + conda: + "../envs/mc_base.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + PYTHONPATH="" # Issue #1031 (https://bitbucket.org/snakemake/snakemake/issues/1031) + python workflow/scripts/segmentation_scripts/detect_strand_states.py \ + --sce_min_distance {config[sce_min_distance]} \ + --sce_add_cutoff {config[additional_sce_cutoff]} \ + --min_diff_jointseg {config[min_diff_jointseg]} \ + --min_diff_singleseg {config[min_diff_singleseg]} \ + --output_jointseg {output.jointseg} \ + --output_singleseg {output.singleseg} \ + --output_strand_states {output.strand_states} \ + --samplename {wildcards.sample} \ + --cellnames {params.cellnames} \ + {input.info} \ + {input.counts} \ + {input.jointseg} \ + {input.singleseg} > {log} 2>&1 + """ + diff --git a/workflow/rules/setup.smk b/workflow/rules/setup.smk new file mode 100644 index 00000000..8692fefe --- /dev/null +++ b/workflow/rules/setup.smk @@ -0,0 +1,14 @@ +rule install_rlib_strandphaser: + output: + check = touch(config['output_location'] + 'strandphaser/R_setup/strandphaser_version-{}.ok'.format(config['git_commit_strandphaser'])) + log: + config['output_location'] + 'log/strandphaser/strandphaser_install.log' + conda: + "../envs/rtools.yaml" + resources: + mem_mb = "10G", + params: + version = config['git_commit_strandphaser'], + repo = config['git_repo_strandphaser'] + shell: + 'LC_CTYPE=C TAR=$(which tar) Rscript workflow/scripts/strandphaser_scripts/install_strandphaser.R {params.version} {params.repo} > {log} 2>&1' diff --git a/workflow/rules/stats.smk b/workflow/rules/stats.smk new file mode 100644 index 00000000..76e860c8 --- /dev/null +++ b/workflow/rules/stats.smk @@ -0,0 +1,59 @@ +import pandas as pd +config_df = pd.read_csv(config["output_location"] + "config/config_df.tsv", sep="\t") +samples = sorted(config_df.Sample.unique().tolist()) + +################################################################################ +# Summary statistics on sv calls # +################################################################################ + +rule summary_statistics: + input: + segmentation = config["output_location"] + 'segmentation/{sample}/Selection_jointseg.txt', + strandstates = config["output_location"] + 'segmentation/{sample}/Selection_initial_strand_state', + sv_calls = config["output_location"] + 'mosaiclassifier/sv_calls/{sample}/{method}_filter{filter}.tsv', + complex = config["output_location"] + "mosaiclassifier/sv_calls/{sample}/{method}_filter{filter}.complex.tsv", + merged = config["output_location"] + "mosaiclassifier/postprocessing/merge/{sample}/{method}.tsv", + output: + tsv = config["output_location"] + 'stats/{sample}/{method}_filter{filter}.tsv', + log: + config["output_location"] + 'log/summary_statistics/{sample}/{method}_filter{filter}.log' + run: + p = [] + try: + f = config["ground_truth_clonal"][wildcards.sample] + if len(f) > 0: + p.append('--true-events-clonal') + p.append(f) + except KeyError: + pass + try: + f = config["ground_truth_single_cell"][wildcards.sample] + if len(f) > 0: + p.append('--true-events-single-cell') + p.append(f) + except KeyError: + pass + if wildcards.filter == 'TRUE': + p.append('--merged-file') + p.append(input.merged) + additional_params = ' '.join(p) + shell('workflow/scripts/stats/callset_summary_stats.py --segmentation {input.segmentation} --strandstates {input.strandstates} --complex-regions {input.complex} {additional_params} {input.sv_calls} > {output.tsv} ') + +rule aggregate_summary_statistics: + input: + tsv=expand(config["output_location"] + "stats/{sample}/{method}.tsv", method=config['methods'], sample=samples), + output: + # tsv=report(config["output_location"] + "stats/{sample}/stats-merged.tsv", category="Stats", labels={"Type" : "Complete stats"}) + tsv=config["output_location"] + "stats/{sample}/stats-merged.tsv" + shell: + "(head -n1 {input.tsv[0]} && (tail -n1 -q {input.tsv} | sort -k1) ) > {output}" + +# rule summary_statistics_to_pdf: +# input: +# config["output_location"] + "stats/{sample}/stats-merged.tsv" +# output: +# report(config["output_location"] + "stats/{sample}/stats-merged.pdf", category="Stats", labels={"Type" : "Complete stats"}) +# run: +# import pandas as pd +# df = pd.read_csv(input[0], sep='\t') +# df.to_pdf \ No newline at end of file diff --git a/workflow/rules/strandphaser.smk b/workflow/rules/strandphaser.smk new file mode 100644 index 00000000..c2d1f36e --- /dev/null +++ b/workflow/rules/strandphaser.smk @@ -0,0 +1,216 @@ +from workflow.scripts.utils.utils import get_mem_mb + +################################################################################ +# StrandPhaseR things # +################################################################################ + +rule convert_strandphaser_input: + """ + rule fct: extract only segmentation with WC orientation + input: initial_strand_state file coming from rule segmentation_selection & info file from mosaic count output + output: filtered TSV file with start/end coordinates of WC-orientated segment to be used by strandphaser + """ + input: + states = config["output_location"] + "segmentation/{sample}/Selection_initial_strand_state", + info = config["output_location"] + "counts/{sample}/{sample}.info" + output: + config["output_location"] + "strandphaser/{sample}/strandphaser_input.txt" + log: + config["output_location"] + "log/strandphaser/convert_strandphaser_input/{sample}.log" + conda: + "../envs/rtools.yaml" + script: + "../scripts/strandphaser_scripts/helper.convert_strandphaser_input.R" + +checkpoint determine_sex_per_cell: + """ + rule fct: + input: + output: + """ + input: + config["input_bam_location"] + "{sample}/selected/" + output: + sex_analysis_cellwise = config["output_location"] + "config/{sample}/sex_analysis_cells.tsv", + sex_analysis_samplewise = config["output_location"] + "config/{sample}/sex_analysis_sample.txt" + log: + config["output_location"] + "log/strandphaser/determine_sex_per_cell/{sample}.log" + conda: + "../envs/mc_base.yaml" + script: + "../scripts/utils/chrxy_analysis.py" + + +def aggregate_phased_haps(wildcards): + with checkpoints.determine_sex_per_cell.get(sample=wildcards.sample).output.sex_analysis_samplewise.open() as f: + sex = f.read().strip().split('\t')[1] + if sex == "M": + config["chromosomes"] = [c for c in config["chromosomes"] if c not in ["chrX", "chrY"]] + elif sex == "F": + config["chromosomes"] = [c for c in config["chromosomes"] if c not in ["chrY"]] + return expand(config["output_location"] + "strandphaser/{{sample}}/StrandPhaseR_analysis.{chrom}/Phased/phased_haps.txt", chrom=config["chromosomes"]) + + + +def aggregate_vcf_gz(wildcards): + with checkpoints.determine_sex_per_cell.get(sample=wildcards.sample).output.sex_analysis_samplewise.open() as f: + sex = f.read().strip().split('\t')[1] + if sex == "M": + config["chromosomes"] = [c for c in config["chromosomes"] if c not in ["chrX", "chrY"]] + elif sex == "F": + config["chromosomes"] = [c for c in config["chromosomes"] if c not in ["chrY"]] + return expand(config["output_location"] + "strandphaser/{{sample}}/StrandPhaseR_analysis.{chrom}/VCFfiles/{chrom}_phased.vcf.gz", chrom=config["chromosomes"]) + + + +def aggregate_vcf_gz_tbi(wildcards): + with checkpoints.determine_sex_per_cell.get(sample=wildcards.sample).output.sex_analysis_samplewise.open() as f: + sex = f.read().strip().split('\t')[1] + if sex == "M": + config["chromosomes"] = [c for c in config["chromosomes"] if c not in ["chrX", "chrY"]] + elif sex == "F": + config["chromosomes"] = [c for c in config["chromosomes"] if c not in ["chrY"]] + return expand(config["output_location"] + "strandphaser/{{sample}}/StrandPhaseR_analysis.{chrom}/VCFfiles/{chrom}_phased.vcf.gz.tbi", chrom=config["chromosomes"]) + + + +# TODO : replace by clean config file if possible or by temporary removed file +rule prepare_strandphaser_config_per_chrom: + """ + rule fct: prepare config file used by strandphaser + input: input used only for wildcards : sample, window & bpdens + output: config file used by strandphaser + """ + input: + config["output_location"] + "segmentation/{sample}/Selection_initial_strand_state" + output: + config["output_location"] + "strandphaser/{sample}/StrandPhaseR.{chrom}.config" + run: + with open(output[0], "w") as f: + print("[General]", file = f) + print("numCPU = 1", file = f) + print("chromosomes = '" + wildcards.chrom + "'", file = f) + if (config["paired_end"]): + print("pairedEndReads = TRUE", file = f) + else: + print("pairedEndReads = FALSE", file = f) + print("min.mapq = 10", file = f) + print("", file = f) + print("[StrandPhaseR]", file = f) + print("positions = NULL", file = f) + print("WCregions = NULL", file = f) + print("min.baseq = 20", file = f) + print("num.iterations = 2", file = f) + print("translateBases = TRUE", file = f) + print("fillMissAllele = NULL", file = f) + print("splitPhasedReads = TRUE", file = f) + print("compareSingleCells = TRUE", file = f) + print("callBreaks = FALSE", file = f) + print("exportVCF = '", wildcards.sample, "'", sep = "", file = f) + print("bsGenome = '", config["R_reference"], "'", sep = "", file = f) + + + + +rule run_strandphaser_per_chrom: + """ + rule fct: run strandphaser for each chromosome + input: strandphaser_input.txt from rule convert_strandphaser_input ; genotyped snv for each chrom by freebayes ; configfile created by rule prepare_strandphaser_config_per_chrom ; bam folder + output: + """ + input: + wcregions = config["output_location"] + "strandphaser/{sample}/strandphaser_input.txt", + snppositions = config["output_location"] + "snv_genotyping/{sample}/{chrom}.vcf", + configfile = config["output_location"] + "strandphaser/{sample}/StrandPhaseR.{chrom}.config", + bamfolder = config["input_bam_location"] + "{sample}/selected", + # TODO : tmp solution + strandphaser_install = config['output_location'] + 'strandphaser/R_setup/strandphaser_version-{}.ok'.format(config['git_commit_strandphaser']) + output: + config["output_location"] + "strandphaser/{sample}/StrandPhaseR_analysis.{chrom}/Phased/phased_haps.txt", + config["output_location"] + "strandphaser/{sample}/StrandPhaseR_analysis.{chrom}/VCFfiles/{chrom}_phased.vcf", + # config["output_location"] + "strandphaser/{sample}/StrandPhaseR_analysis.{chrom}/SingleCellHaps/{chrom}_singleCellHaps.pdf", + report( + config["output_location"] + "strandphaser/{sample}/StrandPhaseR_analysis.{chrom}/SingleCellHaps/{chrom}_singleCellHaps.pdf", + category="StrandPhaseR", + subcategory = "{sample}", + caption="../report/strandphaser_haplotypes.rst", + labels={"Sample" : "{sample}", "Chrom" : "{chrom}"} + ) + log: + config["output_location"] + "log/run_strandphaser_per_chrom/{sample}/{chrom}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + shell: + # {config[Rscript]} + """ + Rscript workflow/scripts/strandphaser_scripts/StrandPhaseR_pipeline.R \ + {input.bamfolder} \ + {config[output_location]}strandphaser/{wildcards.sample}/StrandPhaseR_analysis.{wildcards.chrom} \ + {input.configfile} \ + {input.wcregions} \ + {input.snppositions} \ + $(pwd)/utils/R-packages/ > {log} 2>&1 + """ + +rule merge_strandphaser_vcfs: + input: + ## OLD calling + # vcfs=expand(config["output_location"] + "strandphaser/{{sample}}/StrandPhaseR_analysis.{chrom}/VCFfiles/{chrom}_phased.vcf.gz", chrom=config["chromosomes"]), + # tbis=expand(config["output_location"] + "strandphaser/{{sample}}/StrandPhaseR_analysis.{chrom}/VCFfiles/{chrom}_phased.vcf.gz.tbi", chrom=config["chromosomes"]), + ## NEW calling that takes into account sex sample (see checkpoint determine_sex_per_cell) + vcfs= ancient(aggregate_vcf_gz), + tbis= ancient(aggregate_vcf_gz_tbi) + output: + vcfgz=config["output_location"] + "strandphaser/phased-snvs/{sample}.vcf.gz" + log: + config["output_location"] + "log/merge_strandphaser_vcfs/{sample}.log" + conda: + "../envs/mc_bioinfo_tools.yaml" + resources: + mem_mb = get_mem_mb, + shell: + """ + (bcftools concat -a {input.vcfs} | bcftools view -o {output.vcfgz} -O z --genotype het --types snps - ) > {log} 2>&1 + """ + + + +rule combine_strandphaser_output: + input: + aggregate_phased_haps + output: + config["output_location"] + "strandphaser/{sample}/strandphaser_phased_haps_merged.txt" + log: + config["output_location"] + "log/combine_strandphaser_output/{sample}.log" + resources: + mem_mb = get_mem_mb, + run: + ## Errors on slurm with previous version + # """ + # set -o pipefail; + # cat {input} | head -n1 > {output}; + # tail -q -n+2 {input} >> {output}; > {log} 2>&1 + # """ + ## New version using pandas + import pandas as pd + pd.concat([pd.read_csv(file, sep="\t") for j, file in enumerate(input)]).to_csv(output[0], sep="\t", index=False) + + + +rule convert_strandphaser_output: + input: + phased_states = config["output_location"] + "strandphaser/{sample}/strandphaser_phased_haps_merged.txt", + initial_states = config["output_location"] + "segmentation/{sample}/Selection_initial_strand_state", + info = config["output_location"] + "counts/{sample}/{sample}.info" + output: + config["output_location"] + "strandphaser/{sample}/StrandPhaseR_final_output.txt" + log: + config["output_location"] + "log/convert_strandphaser_output/{sample}.log" + conda: + "../envs/rtools.yaml" + resources: + mem_mb = get_mem_mb, + script: + "../scripts/strandphaser_scripts/helper.convert_strandphaser_output.R" diff --git a/workflow/rules/utils.smk b/workflow/rules/utils.smk new file mode 100644 index 00000000..e44fdc2b --- /dev/null +++ b/workflow/rules/utils.smk @@ -0,0 +1,61 @@ +from workflow.scripts.utils.utils import get_mem_mb + +################################################################################ +# UTILS # +################################################################################ + +rule index_bam: + """ + rule fct: + input: + output: + """ + input: + "{file}.bam" + output: + "{file}.bam.bai" + log: + "{file}.bam.log" + conda: + "../envs/mc_bioinfo_tools.yaml" + resources: + mem_mb = get_mem_mb, + shell: + "samtools index {input} > {log} 2>&1" + + +rule compress_vcf: + """ + rule fct: + input: + output: + """ + input: + vcf="{file}.vcf", + output: + vcf="{file}.vcf.gz", + log: + "{file}.vcf.gz.log" + conda: + "../envs/mc_bioinfo_tools.yaml" + shell: + "bgzip {input.vcf} > {log} 2>&1" + +rule index_vcf: + """ + rule fct: + input: + output: + """ + input: + vcf="{file}.vcf.gz", + output: + vcf="{file}.vcf.gz.tbi", + log: + "{file}.vcf.gz.tbi.log" + conda: + "../envs/mc_bioinfo_tools.yaml" + shell: + "tabix -p vcf {input.vcf} > {log} 2>&1" + + diff --git a/workflow/scripts/Mosaic/src/CMakeLists.txt b/workflow/scripts/Mosaic/src/CMakeLists.txt new file mode 100644 index 00000000..3ba57db0 --- /dev/null +++ b/workflow/scripts/Mosaic/src/CMakeLists.txt @@ -0,0 +1,95 @@ +cmake_minimum_required (VERSION 3.1) +set(CMAKE_CXX_STANDARD 11) +set(CMAKE_CXX_STANDARD_REQUIRED ON) + +project (Mosaicatcher CXX) + + +include(ExternalProject) + + +## Disable in-source builds +if("${PROJECT_SOURCE_DIR}" STREQUAL "${PROJECT_BINARY_DIR}") + message(FATAL_ERROR "In-source builds are not allowed. Please choose a different build path.") +endif("${PROJECT_SOURCE_DIR}" STREQUAL "${PROJECT_BINARY_DIR}") + + + +## Link Boost +find_package(Boost 1.50 COMPONENTS system date_time filesystem iostreams program_options REQUIRED) +include_directories (${Boost_INCLUDE_DIR}) + + + +## Link HTSlib using PackageConfig +find_package(PkgConfig REQUIRED) + +pkg_check_modules(HTSlib REQUIRED htslib) +set(HTSlib_LIBRARIES "${HTSlib_LDFLAGS}") +message(STATUS " HTSlib include dirs: ${HTSlib_INCLUDE_DIRS}") +message(STATUS " HTSlib libraries: ${HTSlib_LIBRARIES}") +### Set version +# Get the current working branch +execute_process( + COMMAND git rev-parse --abbrev-ref HEAD + WORKING_DIRECTORY ${CMAKE_SOURCE_DIR} + OUTPUT_VARIABLE GIT_BRANCH + OUTPUT_STRIP_TRAILING_WHITESPACE +) +# Get the latest abbreviated commit hash of the working branch +execute_process( + COMMAND git log -1 --format=%h + WORKING_DIRECTORY ${CMAKE_SOURCE_DIR} + OUTPUT_VARIABLE GIT_COMMIT_HASH + OUTPUT_STRIP_TRAILING_WHITESPACE +) +# 1. Set Mosaicatcher version +set (MOSAIC_VERSION_MAJOR 0) +set (MOSAIC_VERSION_MINOR 3) +# 2. Boost version from find_package (Boost_VERSION) +# 3. Get HTSlib version from git +execute_process( + COMMAND git describe --always + WORKING_DIRECTORY ${PROJECT_BINARY_DIR}/htslib + OUTPUT_VARIABLE HTSLIB_VERSION + OUTPUT_STRIP_TRAILING_WHITESPACE +) +set(HTSLIB_VERSION "1.3.1 ${HTSLIB_VERSION}") +# 4. write version.hpp file +configure_file ( + "${PROJECT_SOURCE_DIR}/version.hpp.in" + "${PROJECT_BINARY_DIR}/version.hpp" + ) +# 5. Include build direcotry so version.hpp can be found +include_directories("${PROJECT_BINARY_DIR}") # to find version.hpp +### Compiler flags +add_definitions(-Wall -pedantic -Wno-unknown-pragmas) + +set(HEADER_FILES + count.hpp + counter.hpp + distribution.hpp + hmm.hpp + intervals.hpp + iocounts.hpp + program_options.hpp + segmentation.hpp + simulate.hpp + utils.hpp) +### Add executable +add_executable (mosaic main.cpp ${HEADER_FILES}) +target_link_libraries (mosaic LINK_PUBLIC ${Boost_LIBRARIES}) +target_link_libraries (mosaic LINK_PUBLIC hts) +### Doxygen API documentation +find_package(Doxygen) +if(DOXYGEN_FOUND) + # write version into doxygen file + configure_file(${CMAKE_CURRENT_SOURCE_DIR}/doc/Doxyfile.in ${CMAKE_CURRENT_BINARY_DIR}/doc/Doxyfile @ONLY) + add_custom_target(doc ALL + COMMAND ${DOXYGEN_EXECUTABLE} ${CMAKE_CURRENT_BINARY_DIR}/doc/Doxyfile + WORKING_DIRECTORY ${CMAKE_CURRENT_BINARY_DIR}/doc + COMMENT "Generating API documentation with Doxygen" VERBATIM + ) +else() + message("Need Doxygen to produce API documentation. This is skipped.") +endif(DOXYGEN_FOUND) \ No newline at end of file diff --git a/workflow/scripts/Mosaic/src/calc_bins.hpp b/workflow/scripts/Mosaic/src/calc_bins.hpp new file mode 100644 index 00000000..0b5ffd8c --- /dev/null +++ b/workflow/scripts/Mosaic/src/calc_bins.hpp @@ -0,0 +1,186 @@ +/* + Copyright (C) 2017 Sascha Meiers + Distributed under the MIT software license, see the accompanying + file LICENSE.md or http://www.opensource.org/licenses/mit-license.php. + */ + +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include + + +struct Conf_calc_bins { + boost::filesystem::path f_in; + boost::filesystem::path f_out; + boost::filesystem::path f_excl; + unsigned minMapQual; + unsigned num_reads; + unsigned window; +}; + +int main_calc_bins(int argc, char **argv) +{ + Conf_calc_bins conf; + boost::program_options::options_description generic("Generic options"); + generic.add_options() + ("help,?", "show help message") + ("mapq,q", boost::program_options::value(&conf.minMapQual)->default_value(10), "min mapping quality") + ("window,w", boost::program_options::value(&conf.window)->default_value(100000), "window size to approximate") + ("numreads,n", boost::program_options::value(&conf.num_reads)->default_value(20), "sample 1/n of reads (reduce memory)") + ("out,o", boost::program_options::value(&conf.f_out)->default_value("bins.bed"), "output file for bins") + ("exclude,x", boost::program_options::value(&conf.f_excl), "Exclude chromosomes (no regions!)") + ; + + boost::program_options::options_description hidden("Hidden options"); + hidden.add_options() + ("input-file", boost::program_options::value(&conf.f_in), "input bam file (WGS or merged libraries)") + ; + + boost::program_options::positional_options_description pos_args; + pos_args.add("input-file", -1); + + boost::program_options::options_description cmdline_options; + cmdline_options.add(generic).add(hidden); + boost::program_options::options_description visible_options; + visible_options.add(generic); + boost::program_options::variables_map vm; + + boost::program_options::store(boost::program_options::command_line_parser(argc, argv).options(cmdline_options).positional(pos_args).run(), vm); + boost::program_options::notify(vm); + + // Check command line arguments + if (vm.count("help") || (!vm.count("input-file"))) { + std::cout << "Usage: " << argv[0] << " [OPTIONS] " << std::endl; + std::cout << visible_options << "\n"; + if(vm.count("help")) { + std::cout << "Specify whole genome sequencing data (or a set of Strand-seq cells" << std::endl; + std::cout << "merged into a single file) which were sequenced under equal conditions." << std::endl; + std::cout << "This tool will create bins of variable width but which contian the same" << std::endl; + std::cout << "number of reads. This way we hope to overcome mappability issues." << std::endl; + std::cout << std::endl; + } + return vm.count("help") ? 0 : 1; + } + + // Open bam file + samFile* samfile = sam_open(conf.f_in.string().c_str(), "r"); + if (samfile == NULL) { + std::cerr << "Fail to open file " << conf.f_in.string() << std::endl; + return 1; + } + hts_idx_t* idx = sam_index_load(samfile, conf.f_in.string().c_str()); + if (idx == NULL) { + std::cerr << "Fail to open index for " << conf.f_in.string() << std::endl; + return 1; + } + bam_hdr_t* hdr = sam_hdr_read(samfile); + + // Exclude chromosomes: + std::set excl_chroms; + if (vm.count("exclude")) { + std::string line; + std::ifstream excl_file(conf.f_excl.string().c_str()); + if (excl_file.is_open()) { + while ( getline (excl_file, line) ) { + boost::trim(line); + if (line.size()>0) + excl_chroms.insert(line); + } + excl_file.close(); + } else { + std::cerr << "Fail to open exclude file " << conf.f_excl.string() << std::endl; + return 1; + } + } + + unsigned n_chroms = 0; + for (int chrom = 0; chrom < hdr->n_targets; ++chrom) + if (!excl_chroms.count(hdr->target_name[chrom])) + ++n_chroms; + boost::progress_display show_progress( n_chroms ); + + + // Save all positions in vector (sampled to every n-th position) + std::vector > positions; + std::vector tids; + for (int chrom = 0; chrom < hdr->n_targets; ++chrom) { + + // Skip unused chromosomes + if (excl_chroms.count(hdr->target_name[chrom])) continue; + positions.push_back(std::vector()); + tids.push_back(chrom); + + ++show_progress; + unsigned n_reads = 0; + hts_itr_t* iter = sam_itr_queryi(idx, chrom, 0, hdr->target_len[chrom]); + bam1_t* rec = bam_init1(); + while (sam_itr_next(samfile, iter, rec) >= 0) { + + if (rec->core.flag & (BAM_FSECONDARY | BAM_FQCFAIL | BAM_FDUP | BAM_FSUPPLEMENTARY | BAM_FUNMAP)) + continue; + if ((rec->core.qual < conf.minMapQual) || (rec->core.tid<0)) + continue; + if (++n_reads == conf.num_reads) { + positions[chrom].push_back(rec->core.pos); + n_reads = 0; + } + } + bam_destroy1(rec); + hts_itr_destroy(iter); + } + hts_idx_destroy(idx); + sam_close(samfile); + + + // 'positions' only contains non-exlcuded chromosomes + // 'tids' contains matching tids. + + // Calculate average number of reads per window-size + uint64_t total_num = 0; + uint64_t total_len = 0; + for (unsigned i = 0; itarget_len[ tids[i] ]; + } + float avg_num_reads = total_num/(float)total_len * conf.window; + unsigned jump = std::round(avg_num_reads/(float)conf.num_reads); + if (jump < 1) jump = 1; + + + std::cout << "Approximated window size wished: " << conf.window/1000 << "kb." << std::endl; + std::cout << "Input data contains: " << avg_num_reads << " reads per " << conf.window/1000 << "kb." << std::endl; + std::cout << "Genome will be split every " << jump * conf.num_reads << " reads (divergence due to sampling)." << std::endl; + std::cout << "Writing to " << conf.f_out.string() << std::endl; + + // Prepare output file + std::ofstream out(conf.f_out.string()); + if (!out.is_open()) { + std::cerr << "Fail to open file " << conf.f_out.string() << std::endl; + return 1; + } + + // Split positions every ~avg_num_reads and output + for (unsigned i=0; itarget_name[ tids[i] ] << "\t" << prev_pos << "\t" << positions[i][list_pos] << std::endl; + prev_pos = positions[i][list_pos]; + } + // last bin: + out << hdr->target_name[ tids[i] ] << "\t" << prev_pos << "\t" << hdr->target_len[ tids[i] ] << std::endl; + } + + out.close(); + return 0; +} + diff --git a/workflow/scripts/Mosaic/src/count.hpp b/workflow/scripts/Mosaic/src/count.hpp new file mode 100644 index 00000000..1bbb1268 --- /dev/null +++ b/workflow/scripts/Mosaic/src/count.hpp @@ -0,0 +1,426 @@ +/* + Copyright (C) 2017 Sascha Meiers + Distributed under the MIT software license, see the accompanying + file LICENSE.md or http://www.opensource.org/licenses/mit-license.php. + */ + +#include +#include +#include +#include +#include + +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include +#include + +#include "version.hpp" +#include "intervals.hpp" +#include "counter.hpp" +#include "distribution.hpp" +#include "hmm.hpp" +#include "iocounts.hpp" + + +/** + * @file + * @defgroup count Bin, count and classify W/C reads. + * + * Summary of how Strand-seq data is binned, counted and classified. + * + * ## Strand-seq read counting + * + * @todo write documentation about counting. +*/ + + +using interval::Interval; +using count::TGenomeCounts; +using count::Counter; + + +struct Conf { + std::vector f_in; + boost::filesystem::path f_out; + boost::filesystem::path f_bins; + boost::filesystem::path f_excl; + boost::filesystem::path f_info; + boost::filesystem::path f_sample_info; + boost::filesystem::path f_removed_bins; + boost::filesystem::path f_segments; + int minMapQual; + unsigned int window; + std::string mode; +}; + + +/** + * + */ +void run_standard_HMM(std::vector & counts, + std::vector const & good_cells, + std::vector & cells, + std::vector const & good_bins, + std::vector const & good_map, + std::unordered_map const & samples, + float p_trans) +{ + // Set up and run HMM: + hmm::HMM > hmm({"CC", "WC", "WW"}); + hmm.set_initials({0.3333, 0.3333, 0.3333}); + hmm.set_transitions({1-2*p_trans, p_trans, p_trans, \ + p_trans, 1-2*p_trans, p_trans, \ + p_trans, p_trans, 1-2*p_trans}); + + for (auto i = good_cells.begin(); i != good_cells.end(); ++i) + { + // set NB(n,p) parameters according to `p` of sample and mean of cell. + float p = samples.at(cells[*i].sample_name).p; + float n = (float)cells[*i].mean_bin_count * p / (1-p); + float a = 0.1; + cells[*i].nb_p = p; + cells[*i].nb_r = n; + cells[*i].nb_a = a; + + //std::cout << "NB parameters for cell " << ": p=" << p << "\tn=" << n << "\tz=" << z << std::endl; + + hmm.set_emissions( {\ + hmm::MultiVariate({hmm::NegativeBinomial(p, (1-a)*n), hmm::NegativeBinomial(p, a*n)}), // CC + hmm::MultiVariate({hmm::NegativeBinomial(p, n/2), hmm::NegativeBinomial(p, n/2)}), // WC + hmm::MultiVariate({hmm::NegativeBinomial(p, a*n), hmm::NegativeBinomial(p, (1-a)*n)}) // WW + }); + run_HMM(hmm, counts[*i], good_bins, good_map); + } +} + + + + + +int main_count(int argc, char **argv) +{ + + // Command line options + Conf conf; + boost::program_options::options_description generic("Generic options"); + generic.add_options() + ("help,?", "show help message") + ("verbose,v", "Be more verbose in the output") + ("mapq,q", boost::program_options::value(&conf.minMapQual)->default_value(10), "min mapping quality") + ("window,w", boost::program_options::value(&conf.window)->default_value(500000), "window size of fixed windows") + ("out,o", boost::program_options::value(&conf.f_out)->default_value("out.txt.gz"), "output file for counts + strand state (gz)") + ("bins,b", boost::program_options::value(&conf.f_bins), "BED file with manual bins (disables -w). See also 'makebins'") + ("exclude,x", boost::program_options::value(&conf.f_excl), "Exclude chromosomes and regions") + ("info,i", boost::program_options::value(&conf.f_info), "Write info about samples") + ("do-not-filter-by-WC", "When black-listing bins, only consider coverage and not WC/WW/CC states") + ("do-not-blacklist-hmm", "Do not output a blacklist (None bins). Bins will be blacklisted for parameter estimation, but not during HMM") + ; + // Note: Currently the blacklisting is done after counting via R/norm.R. A better way would be to + // input the blacklist + normalization into MosaiCatcher during the counting, then the HMM could be run + // only on non-blacklisted bins. Now it is run through many bad bin that potentially affect the quality of results. + + boost::program_options::options_description hidden("Hidden options"); + hidden.add_options() + ("input-file", boost::program_options::value >(&conf.f_in), "input bam file(s)") + ("sample_info,S", boost::program_options::value(&conf.f_sample_info), "write info per sample") + ("removed_bins,R", boost::program_options::value(&conf.f_removed_bins), "bins that were removed (bed file)") + ; + + boost::program_options::positional_options_description pos_args; + pos_args.add("input-file", -1); + + boost::program_options::options_description cmdline_options; + cmdline_options.add(generic).add(hidden); + boost::program_options::options_description visible_options; + visible_options.add(generic); + boost::program_options::variables_map vm; + + boost::program_options::store(boost::program_options::command_line_parser(argc, argv).options(cmdline_options).positional(pos_args).run(), vm); + boost::program_options::notify(vm); + + // Check command line arguments + if (!vm["window"].defaulted() && vm.count("bins")) { + std::cerr << "[Error] -w and -b cannot be specified together" << std::endl << std::endl; + goto print_usage_and_exit; + } + if (vm.count("bins") && vm.count("exclude")) { + std::cerr << "[Error] Exclude chromosomes (-x) have no effect when -b is specified. Stop" << std::endl << std::endl; + goto print_usage_and_exit; + } + + if (vm.count("help") || !vm.count("input-file")) + { + print_usage_and_exit: + std::cout << std::endl; + std::cout << "Mosaicatcher " << STRINGIFYMACRO(MOSAIC_VERSION_MAJOR); + std::cout << "." << STRINGIFYMACRO(MOSAIC_VERSION_MINOR) << std::endl; + std::cout << "> Count reads from Strand-seq BAM files." << std::endl; + std::cout << std::endl; + std::cout << "Usage: " << argv[0] << " [OPTIONS] ..." << std::endl << std::endl; + std::cout << visible_options << std::endl; + std::cout << "Notes:" << std::endl; + std::cout << " * writes a table of bin counts and state classifcation as a gzip file (default: out.txt.gz)" << std::endl; + std::cout << " * Reads are counted by start position" << std::endl; + std::cout << " * One cell per BAM file, including SM tag in header" << std::endl; + std::cout << " * For paired-end data, only read 1 is counted" << std::endl; + return vm.count("help") ? 0 : 1; + } + + + /////////////////////////////////////////////////////////// global variables + /* leave one BAM header open to get chrom names & lengths */ + bam_hdr_t* hdr = NULL; + + /* regarding each cell */ + std::vector cells(conf.f_in.size()); + std::vector counts(conf.f_in.size()); + std::vector good_cells; + + /* regarding each sample */ + std::unordered_map samples; + + /* regarding bins */ + std::vector bins; + std::vector chrom_map; + std::vector good_bins; + std::vector good_map; + //////////////////////////////////////////////////////////////////////////// + + + + // + // Chapter: Binning & counting + // =========================== + // + + // Read sample names from headers. + // Keep one header throughout the program. + if (vm.count("verbose")) std::cout << "[Info] Exploring SAM headers..." << std::endl; + for(unsigned i = 0; i < conf.f_in.size(); ++i) + { + cells[i].id = (int32_t)i; + cells[i].bam_file = conf.f_in[i].string(); + samFile* samfile = sam_open(conf.f_in[i].string().c_str(), "r"); + if (samfile == NULL) { + std::cerr << "[Error] Fail to open file " << conf.f_in[i].string() << std::endl; + return 1; + } + hdr = sam_hdr_read(samfile); + if (!get_RG_tag("SM", hdr->text, cells[i].sample_name)) { + std::cerr << "[Error] Each BAM file has to have exactly one RG tag. Group cells " << std::endl; + std::cerr << " belonging to the same sample by the SM tag." << std::endl; + std::cerr << " Problematic file: " << conf.f_in[i].string() << std::endl << std::endl; + goto print_usage_and_exit; + } + if (!get_RG_tag("ID", hdr->text, cells[i].cell_name, /*allow_multiple_matches = */ true)) { + std::cerr << "[Error] Each BAM file has to have exactly one RG tag." << std::endl; + std::cerr << " Problematic file: " << conf.f_in[i].string() << std::endl; + goto print_usage_and_exit; + } + sam_close(samfile); + } + + + // Bin the genome + unsigned median_binsize; + chrom_map = std::vector(hdr->n_targets, -1); + if (vm.count("bins")) + { + if (!read_dynamic_bins(bins, + chrom_map, + conf.f_bins.string().c_str(), + hdr)) + return 1; + TMedianAccumulator med_acc; + for (Interval const & b : bins) + med_acc(b.end - b.start); + median_binsize = boost::accumulators::median(med_acc); + std::cout << "[Info] Reading " << bins.size() << " variable-width bins with median bin size of " << round(median_binsize/1000) << "kb" << std::endl; + } + else + { + std::vector exclude; + if (vm.count("exclude")) { + read_exclude_file(conf.f_excl.string(), hdr, exclude, vm.count("verbose")); + sort(exclude.begin(), exclude.end(), interval::invt_less); + } + std::cout << "[Info] Creating " << round(conf.window/1000) << "kb bins with " << exclude.size() << " excluded regions" << std::endl; + create_fixed_bins(bins, + chrom_map, + conf.window, + exclude, + hdr->n_targets, + hdr->target_len); + median_binsize = conf.window; + } + // add last element for easy calculation of number of bins + chrom_map.push_back((int32_t)bins.size()); + + + // Count in bins. If A bam file cannot be read, the cell is ignored and + // the respective entry in `counts` and `cells` will be erased. + std::cout << "[Info] Reading " << conf.f_in.size() << " BAM files..."; + boost::progress_display show_progress1(conf.f_in.size()); + for (unsigned i = 0, i_f = 0; i_f < conf.f_in.size(); ++i, ++i_f) + { + if (!count_sorted_reads(conf.f_in[i_f].string(), + bins, + chrom_map, + hdr, + conf.minMapQual, + counts[i], + cells[i])) + { + std::cerr << "[Warning] Ignoring cell " << conf.f_in[i_f].string() << std::endl; + counts.erase(counts.begin()+i); + cells.erase(cells.begin()+i); + --i; + } + ++show_progress1; + } + + + + + + // + // Chapter: Filter cells and bins and estimate NB parameter p + // ========================================================== + // + + // median per cell + count::set_median_per_cell(counts, cells); + + // filter cells with low counts + good_cells = count::get_good_cells(counts, cells); + + // filter bins with abnormal counts + if (good_cells.size() < 5) { + std::cerr << "[Warning] Only few cells with sufficient coverage. I will not filter bad bins" << std::endl; + good_bins.resize(bins.size()); + std::iota(good_bins.begin(), good_bins.end(), 0); // fill with 0,1,2,... + } else { + good_bins = count::get_good_bins(counts, + cells, + good_cells, + vm.count("verbose"), + !vm.count("do-not-filter-by-WC")); + if (vm.count("verbose")) std::cout << "[Info] Filtered out " << bins.size() - good_bins.size() << " bad bins from " << bins.size() << std::endl; + } + + // build chrom_map for good bins + good_map = std::vector(chrom_map.size() - 1, -1); + int32_t pos = 0; + for (int32_t chr = 0; chr < static_cast(good_map.size()); ++chr) { + while (pos < good_bins.size() && bins[good_bins[pos]].chr < chr) + ++pos; + // now goodit is either at first occurence of chr, or at the end. + if (pos >= good_bins.size()) good_map[chr] = (int32_t)good_bins.size(); + else good_map[chr] = pos; + } + // add last element for easy calculation of number of bins + good_map.push_back((int32_t)good_bins.size()); + + + + // calculate cell means and cell variances, grouped by sample (not cell) + calculate_new_cell_mean(samples, cells, counts, good_cells, good_bins); + + + // Estimation of parameter p per sample (should work even with one cell only) + for (auto it = samples.begin(); it != samples.end(); ++it) { + SampleInfo & s = it->second; + s.p = std::inner_product(s.means.begin(), s.means.end(), s.means.begin(), 0.0f) \ + / std::inner_product(s.means.begin(), s.means.end(), s.vars.begin(), 0.0f); + } + + // Write sample information to file + if (vm.count("sample_info")) { + if (vm.count("verbose")) std::cout << "[Write] sample information: " << conf.f_sample_info.string() << std::endl; + std::ofstream out(conf.f_sample_info.string()); + if (out.is_open()) { + out << "sample\tcells\tp\tmeans\tvars" << std::endl; + for (auto it = samples.begin(); it != samples.end(); ++it) { + SampleInfo const & s = it->second; + out << it->first << "\t" << s.means.size() << "\t" << s.p << "\t" << s.means[0]; + for (unsigned k=1; k normal_bins(bins.size()); + std::iota(normal_bins.begin(), normal_bins.end(), 0); + run_standard_HMM(counts, + good_cells, + cells, + normal_bins, + chrom_map, + samples, + 10.0f / bins.size()); + } else { + run_standard_HMM(counts, + good_cells, + cells, + good_bins, + good_map, + samples, + 10.0f / good_bins.size()); + } + + + + // Print cell information: + if (vm.count("info")) { + if (vm.count("verbose")) std::cout << "[Write] Cell summary: " << conf.f_info.string() << std::endl; + write_cell_info(conf.f_info.string(), cells); + } + + + // Write final counts + classification + std::cout << "[Write] count table: " << conf.f_out.string() << std::endl; + { + // TODO: why do I pass vector? I could make it two separate vectors. Just check where else the io function is called. + struct sample_cell_name_wrapper { + std::vector const & cells; + sample_cell_name_wrapper(std::vector const & cells) : cells(cells) + {} + std::pair operator[](size_t i) const { + return std::make_pair(cells[i].sample_name, cells[i].cell_name); + } + }; + + if (!io::write_counts_gzip(conf.f_out.string(), + counts, + bins, + hdr->target_name, + sample_cell_name_wrapper(cells)) ) + return 1; + } + + return 0; +} diff --git a/workflow/scripts/Mosaic/src/counter.hpp b/workflow/scripts/Mosaic/src/counter.hpp new file mode 100644 index 00000000..16be8261 --- /dev/null +++ b/workflow/scripts/Mosaic/src/counter.hpp @@ -0,0 +1,405 @@ +/* + Copyright (C) 2017 Sascha Meiers + Distributed under the MIT software license, see the accompanying + file LICENSE.md or http://www.opensource.org/licenses/mit-license.php. + */ + +#ifndef counter_hpp +#define counter_hpp + +#include +#include + +#include +#include "utils.hpp" +#include "intervals.hpp" + +namespace count { + + + +using interval::Interval; + + +/** + * @ingroup count + */ +template +struct Counter { + //static const std::vector label_names; + //static const std::map label_id; + TInner watson_count, crick_count; + std::string label; + unsigned n_supplementary; + + Counter() : watson_count(static_cast(0)), crick_count(static_cast(0)), label("None"), n_supplementary(0) + {} + + bool set_label(std::string const & s) { + label = s; + return true; + } + + std::string const & get_label() const { + return label; + } +}; + +/** + * @ingroup count + */ +typedef std::vector> TGenomeCounts; + + +/** + * Calculate median bin count and return list of cells above. + * @ingroup count + * + * **Important:** `cell.median_per_bin` must have been set beforehands! + * + * @param counts Matrix witch raw cell counts - labels are set to "None" if cell has too few reads. + * @param min_median Cutoff for the minimum median read count (dafault: 3). + */ + std::vector get_good_cells(std::vector & counts, + std::vector & cells, + unsigned min_median = 3) +{ + assert(counts.size() == cells.size()); + + std::vector good_cells; + for (unsigned i=0; i= min_median) + good_cells.push_back(i); + else { + cells[i].pass_qc = false; + for (unsigned bin=0; bin < counts[i].size(); ++bin) + counts[i][bin].set_label("None"); + } + } + return good_cells; +} + + +/** + * Write median per cell into CellInfo. + * @ingroup count + * + * @param counts matrix of counts. + * @param cells CellInfo to be written. + */ +void set_median_per_cell(std::vector const & counts, + std::vector & cells) +{ + assert(counts.size() == cells.size()); + + for (unsigned i = 0; i < counts.size(); ++i) { + TMedianAccumulator med_acc; + for (Counter const & count_bin : counts[i]) + med_acc(count_bin.watson_count + count_bin.crick_count); + cells[i].median_bin_count = boost::accumulators::median(med_acc); + } +} + + + + +/** + * Filter bins across all cells. + * @ingroup count + * + * @param counts Matrix witch raw cell counts. + * @param cells Vector of CellInfos. + * @param good_cells Ignore all other cells, e.g. the ones with too few reads. + */ +std::vector get_good_bins(std::vector const & counts, + std::vector const & cells, + std::vector const & good_cells, + bool verbose = 0, + bool filter_by_WC = true) +{ + if (counts.size() < 1) return {}; + + unsigned N = counts[0].size(); + + // Median-Normalized counts + std::vector>> norm_counts(counts.size()); + for (auto i = good_cells.begin(); i != good_cells.end(); ++i) { + norm_counts[*i] = std::vector>(N); + for (unsigned bin = 0; bin < N; ++bin) + norm_counts[*i][bin] = std::make_tuple(counts[*i][bin].watson_count/(float)cells[*i].median_bin_count, + counts[*i][bin].crick_count /(float)cells[*i].median_bin_count); + } + + // mean + variance per bin + std::vector bin_means(N); + std::vector bin_variances(N); + std::vector bin_WC_fraction(N, 0.0); + + if (good_cells.size() < 12) { + std::cerr << "[Warning] Too few cells, I will not filter bins by WC/CC/WW states." << std::endl; + filter_by_WC = false; + } + + for (unsigned bin = 0; bin < N; ++bin) { + TMeanVarAccumulator meanvar_acc; + for (auto i = good_cells.begin(); i != good_cells.end(); ++i) + meanvar_acc(std::get<0>(norm_counts[*i][bin]) + std::get<1>(norm_counts[*i][bin])); + bin_means[bin] = boost::accumulators::mean(meanvar_acc); + bin_variances[bin] = boost::accumulators::variance(meanvar_acc); + + // check how many cells show a WC sginal in this bin + // TODO: this is currently based on fixed cut-offs! + if (filter_by_WC) { + for (auto i = good_cells.begin(); i != good_cells.end(); ++i) { + float bin_WC_frac = std::get<0>(norm_counts[*i][bin]) / (std::get<0>(norm_counts[*i][bin]) + std::get<1>(norm_counts[*i][bin])); + if (bin_WC_frac > 0.15 & bin_WC_frac < 0.85) { + bin_WC_fraction[bin] += 1; + } + } + bin_WC_fraction[bin] /= (float)good_cells.size(); + } + } + + // finding good bins + // to do: this could be extended by checking if bin is always in WC state! + TMeanVarAccumulator meanvar_acc; + std::vector reasons(N,'.'); + std::vector good_bins; + + for (unsigned bin = 0; bin < N; ++bin) + meanvar_acc(bin_means[bin]); + float mean_mean = boost::accumulators::mean(meanvar_acc); + float mean_sd = std::sqrt(boost::accumulators::variance(meanvar_acc)); + + for (unsigned bin = 0; bin < N; ++bin) + { + if (bin_means[bin] < 0.1) { + reasons[bin] = 'l'; // mean too low + // } else if (bin_means[bin] > mean_mean + 4 * mean_sd) { + // reasons[bin] = 'h'; // mean too high + } else if (filter_by_WC && bin_WC_fraction[bin] < 0.05) { + reasons[bin] = 'w'; // never WC + } else if (filter_by_WC && bin_WC_fraction[bin] > 0.95) { + reasons[bin] = 'c'; // always WC + } else { + reasons[bin] = '_'; + good_bins.push_back(bin); + } + } + + if (verbose) { + std::cout << "[Info] Reasons for filtered bins (each character is one bin):" << std::endl; + std::cout << "[...] l = low coverage across all cells" << std::endl; + std::cout << "[...] h = high coverage across all cells" << std::endl; + std::cout << "[...] c = always WC across cells" << std::endl; + std::cout << "[...] w = never WC across cells"; + unsigned x = 0; + while(x < reasons.size()) { + if (x % 80 == 0) std::cout << std::endl << "[...] "; + std::cout << reasons[x++]; + } + std::cout << std::endl; + } + return good_bins; +} + + +std::vector get_good_bins(std::vector const & counts, + std::vector const & cells, + bool verbose = 0, + bool filter_by_WC = true) +{ + // When no good_cells where supplied, use all cells + std::vector good_cells(counts.size()); + for (unsigned i=0; i < good_cells.size(); ++i) + good_cells[i] = i; + + return get_good_bins(counts, cells, good_cells); +} + + + + + + + + + + +/** + * Count reads from BAM file. + * @ingroup count + * + * Count start positions, which are expected to be sorted, into sorted bins. + * Suitable for both fixed and variable-width bins. + */ +bool count_sorted_reads(std::string const & filename, + std::vector const & bins, + std::vector const & chrom_map, + bam_hdr_t * hdr, + int min_map_qual, + TGenomeCounts & counts, + CellInfo & cell) +{ + + // Open bam file + samFile* samfile = sam_open(filename.c_str(), "r"); + if (samfile == NULL) { + std::cerr << "Fail to open file " << filename << std::endl; + return false; + } + hts_idx_t* idx = sam_index_load(samfile, filename.c_str()); + if (idx == NULL) { + std::cerr << "Fail to open index for " << filename << std::endl; + return false; + } + + counts.resize(bins.size(), Counter()); + + // access samfile chrom per chrom + for (int32_t chrom = 0; chrom < hdr->n_targets; ++chrom) { + + // skip chromosomes with no bins + if (chrom_map[chrom+1] - chrom_map[chrom] < 1) + continue; + + int32_t bin = chrom_map[chrom]; + hts_itr_t* iter = sam_itr_queryi(idx, chrom, 0, hdr->target_len[chrom]); + bam1_t* rec = bam_init1(); + while (sam_itr_next(samfile, iter, rec) >= 0) { + + if (rec->core.flag & BAM_FUNMAP) { + continue; + } + ++cell.n_mapped; + + // expect pos to be sorted + int32_t pos = rec->core.pos; + + // skip all bins left of this position. + // Stop when all bins of the chromosome are done + while (pos >= bins[bin].end) + if (bin++ == chrom_map[chrom+1]) + goto end_of_chromosome; + + // Ignore reads before until we reach the start of a bin + if (pos < bins[bin].start) + continue; + + assert(pos >= bins[bin].start && pos < bins[bin].end); + + // Don't read every RG tag because that might slow down BAM parsing. + // auto x = bam_aux_get(rec, "RG"); + + // Ignore certain alignments + if (rec->core.flag & (BAM_FSECONDARY | BAM_FSUPPLEMENTARY | BAM_FQCFAIL)) { + ++cell.n_supplementary; + ++(counts[bin].n_supplementary); + continue; + } if (rec->core.flag & BAM_FDUP) { + ++cell.n_pcr_dups; + continue; + } if ((rec->core.qual < min_map_qual) || (rec->core.tid<0)) { + ++cell.n_low_mapq; + continue; + } if (rec->core.flag & BAM_FREAD2) { + ++cell.n_read2s; + continue; + } + ++cell.n_counted; + if (rec->core.flag & BAM_FREVERSE) + ++( counts[bin].watson_count ); + else + ++( counts[bin].crick_count ); + } + end_of_chromosome: + bam_destroy1(rec); + hts_itr_destroy(iter); + } + + hts_idx_destroy(idx); + sam_close(samfile); + return true; +} + + +/** + * Calculate mean and var. of all cells of a sample based on `good_bins` only. + * @ingroup count + * + * Calculate mean and variance of reads per bin, but only use `good_bins` (this + * make it different from `CellInfo.median_bin_count`). This value is written + * into `CellInfo.mean_bin_count` and also appended to `SampleInfo.means`. + * The values in `SampleInfo.means` and `SampleInfo.vars` will later be used + * to estimate the *p* parameter of the negative binomial. + * + * There is a version requiring `good_cells` and a version without (using all + * cells) + * + * @param samples Map of cell name to sampleInfo --> variables `means` and + * `vars` are updated. + * @param cells List of `CellInfos`, which must have `sample_name` set. Also + * member `mean_bin_count` will be set, but only for `good_cells`. + * @param counts Matrix of raw counts. + * @param good_cells Ignore all other cells. + * @param good_bins Ignore all other bins. Also cell mean and var. is + * re-calculated using only good_cells. + */ +bool calculate_new_cell_mean(std::unordered_map & samples, + std::vector & cells, + std::vector const & counts, + std::vector const & good_cells, + std::vector const & good_bins) +{ + // calculate cell means and cell variances, grouped by sample (not cell) + for (auto i = good_cells.begin(); i != good_cells.end(); ++i) { + + // Get mean and var for this cell, but only from good bins! + TMeanVarAccumulator acc; + for (unsigned bini = 0; bini < good_bins.size(); ++bini) { + acc(counts[*i][good_bins[bini]].crick_count + counts[*i][good_bins[bini]].watson_count); + } + // emplace finds key if existing and returns (it,false); + // otherwise it inserts (key,value) and returns (it,true). + auto it = samples.begin(); + std::tie(it, std::ignore) = samples.emplace(cells[*i].sample_name, SampleInfo()); + float cell_mean = boost::accumulators::mean(acc); + float cell_var = boost::accumulators::variance(acc); + + cells[*i].mean_bin_count = cell_mean; + (it->second).means.push_back(cell_mean); + (it->second).vars.push_back(cell_var); + } + return true; +} +bool calculate_new_cell_mean(std::unordered_map & samples, + std::vector & cells, + std::vector const & counts, + std::vector const & good_bins) +{ + // calculate cell means and cell variances, grouped by sample (not cell) + for (unsigned i = 0; i < counts.size(); ++i) { + + // Get mean and var for this cell, but only from good bins! + TMeanVarAccumulator acc; + for (unsigned bini = 0; bini < good_bins.size(); ++bini) { + acc(counts[i][good_bins[bini]].crick_count + counts[i][good_bins[bini]].watson_count); + } + // emplace finds key if existing and returns (it,false); + // otherwise it inserts (key,value) and returns (it,true). + auto it = samples.begin(); + std::tie(it, std::ignore) = samples.emplace(cells[i].sample_name, SampleInfo()); + float cell_mean = boost::accumulators::mean(acc); + float cell_var = boost::accumulators::variance(acc); + + cells[i].mean_bin_count = cell_mean; + (it->second).means.push_back(cell_mean); + (it->second).vars.push_back(cell_var); + } + return true; +} + + + +} /* namespace */ +#endif /* counter_hpp */ diff --git a/workflow/scripts/Mosaic/src/distribution.hpp b/workflow/scripts/Mosaic/src/distribution.hpp new file mode 100644 index 00000000..ed79f01a --- /dev/null +++ b/workflow/scripts/Mosaic/src/distribution.hpp @@ -0,0 +1,162 @@ +/* + Copyright (C) 2017 Sascha Meiers + Distributed under the MIT software license, see the accompanying + file LICENSE.md or http://www.opensource.org/licenses/mit-license.php. + */ + +#ifndef distribution_h +#define distribution_h + +#include +#include +#include +#include "utils.hpp" +#include +#include +#include + + +namespace hmm { + + + static const double MIN_EMISSION_LOG = -1e4; + // TODO: check for p=0 or 1 in all pdfs --> take special care for log then! + + + /** + * NegativeBinomial + * ---------------- + * p(k) = {\Gamma(n + k) \over \Gamma(k+1) \Gamma(n) } p^n (1-p)^k + * Number of failures k before n-th success. + * Mean := n * (1-p)/p + * Variance := Mean/p + */ + class NegativeBinomial + { + public: + typedef unsigned TEmission; + static const uint8_t dim = 1; + boost::math::negative_binomial nb; + + + NegativeBinomial(double p, double n) : nb(n,p) + {} + + inline double calc_log_emission(std::vector::const_iterator iter) const + { + double pr = log(calc_emission(iter)); + if (pr < MIN_EMISSION_LOG) + pr = MIN_EMISSION_LOG; + return pr; + } + + inline double calc_emission(std::vector::const_iterator iter) const + { + return boost::math::pdf(nb, *iter); + } + }; + + std::ostream& operator<<(std::ostream& os, const NegativeBinomial& obj) + { + os << "Negative Binomial Distribution:" << std::endl; + os << " p = " << obj.nb.success_fraction() << std::endl; + os << " r = " << obj.nb.successes() << std::endl; + os << " mean = " << mean(obj.nb) << std::endl; + os << " var = " << variance(obj.nb) << std::endl; + return os; + } + + + + /** + * MultiVariate + * ------------ + * A quick wrapper to sum up multiple 1-dim distirbutions of the same type into + * one "multivariate" one, in which components are independent. + */ + template + class MultiVariate + { + public: + uint8_t dim; + std::vector inner; + double log_prior; + + MultiVariate(std::vector const & distributions, double _prior = 1) : + inner(distributions), dim(distributions.size()), log_prior(log(_prior)) + { + assert(distributions.size() > 0 && distributions.size() < 256); + for (auto dist : distributions) + assert(dist.dim == (uint8_t)1); + } + + inline double calc_log_emission(typename std::vector::const_iterator iter) const + { + double logp = 0; + for (unsigned i=0; i::const_iterator iter) const + { + return exp(calc_log_emission(iter)); + } + }; + + template + std::ostream& operator<<(std::ostream& os, const MultiVariate& obj) + { + os << "Multivariate distribution with " << (int)obj.dim << " dimensions:" << std::endl; + for (unsigned i=0; i> " << i << ") " << obj.inner[i]; + os << std::endl; + return os; + } + + + + + /** + * CombinedNegBinAndBinomial + * ---------------- + * Model total coverage (w+c) by Negative Binomial and fraction c/(w+c) by + * a binomial distribution. + */ + class CombinedNegBinAndBinomial + { + public: + typedef unsigned TEmission; + static const uint8_t dim = 2; + boost::math::negative_binomial nb; + double ratio; + double prior; + + CombinedNegBinAndBinomial(double nb_p, double nb_r, double strand_ratio, double prior = 1) : + nb(nb_r,nb_p), ratio(strand_ratio), prior(prior) + {} + + inline double calc_log_emission(std::vector::const_iterator iter) const + { + double pr = log(calc_emission(iter)); + if (pr < MIN_EMISSION_LOG) + pr = MIN_EMISSION_LOG; + return pr; + } + + inline double calc_emission(std::vector::const_iterator iter) const + { + unsigned c = *iter++; + unsigned w = *iter++; + boost::math::binomial_distribution<> binomial(c+w, ratio); + return prior * boost::math::pdf(nb, w+c) * boost::math::pdf(binomial, w); + } + }; + + + +} +#endif /* distribution_h */ diff --git a/workflow/scripts/Mosaic/src/doc/Doxyfile.in b/workflow/scripts/Mosaic/src/doc/Doxyfile.in new file mode 100644 index 00000000..5817794a --- /dev/null +++ b/workflow/scripts/Mosaic/src/doc/Doxyfile.in @@ -0,0 +1,2434 @@ +# Doxyfile 1.8.14 + +# This file describes the settings to be used by the documentation system +# doxygen (www.doxygen.org) for a project. +# +# All text after a double hash (##) is considered a comment and is placed in +# front of the TAG it is preceding. +# +# All text after a single hash (#) is considered a comment and will be ignored. +# The format is: +# TAG = value [value, ...] +# For lists, items can also be appended using: +# TAG += value [value, ...] +# Values that contain spaces should be placed between quotes (\" \"). + +#--------------------------------------------------------------------------- +# Project related configuration options +#--------------------------------------------------------------------------- + +DOXYFILE_ENCODING = UTF-8 +PROJECT_NAME = "mosaicatcher" +PROJECT_NUMBER = @MOSAIC_VERSION_MAJOR@.@MOSAIC_VERSION_MINOR@0 +PROJECT_BRIEF = "Calling Structural Variation in Strand-seq data" +PROJECT_LOGO = +OUTPUT_DIRECTORY = @CMAKE_CURRENT_BINARY_DIR@/doc/ +INPUT = @CMAKE_CURRENT_SOURCE_DIR@/ +INPUT_ENCODING = UTF-8 + + + +# standard options: + +# If the CREATE_SUBDIRS tag is set to YES then doxygen will create 4096 sub- +# directories (in 2 levels) under the output directory of each output format and +# will distribute the generated files over these directories. 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For +# instance to make doxygen treat .inc files as Fortran files (default is PHP), +# and .f files as C (default is Fortran), use: inc=Fortran f=C. +# +# Note: For files without extension you can use no_extension as a placeholder. +# +# Note that for custom extensions you also need to set FILE_PATTERNS otherwise +# the files are not read by doxygen. + +EXTENSION_MAPPING = + +# If the MARKDOWN_SUPPORT tag is enabled then doxygen pre-processes all comments +# according to the Markdown format, which allows for more readable +# documentation. See http://daringfireball.net/projects/markdown/ for details. +# The output of markdown processing is further processed by doxygen, so you can +# mix doxygen, HTML, and XML commands with Markdown formatting. Disable only in +# case of backward compatibilities issues. +# The default value is: YES. + +MARKDOWN_SUPPORT = YES + +# When the TOC_INCLUDE_HEADINGS tag is set to a non-zero value, all headings up +# to that level are automatically included in the table of contents, even if +# they do not have an id attribute. +# Note: This feature currently applies only to Markdown headings. +# Minimum value: 0, maximum value: 99, default value: 0. +# This tag requires that the tag MARKDOWN_SUPPORT is set to YES. + +TOC_INCLUDE_HEADINGS = 0 + +# When enabled doxygen tries to link words that correspond to documented +# classes, or namespaces to their corresponding documentation. Such a link can +# be prevented in individual cases by putting a % sign in front of the word or +# globally by setting AUTOLINK_SUPPORT to NO. +# The default value is: YES. + +AUTOLINK_SUPPORT = YES + +# If you use STL classes (i.e. std::string, std::vector, etc.) but do not want +# to include (a tag file for) the STL sources as input, then you should set this +# tag to YES in order to let doxygen match functions declarations and +# definitions whose arguments contain STL classes (e.g. func(std::string); +# versus func(std::string) {}). This also make the inheritance and collaboration +# diagrams that involve STL classes more complete and accurate. +# The default value is: NO. + +BUILTIN_STL_SUPPORT = NO + +# If you use Microsoft's C++/CLI language, you should set this option to YES to +# enable parsing support. +# The default value is: NO. + +CPP_CLI_SUPPORT = NO + +# Set the SIP_SUPPORT tag to YES if your project consists of sip (see: +# https://www.riverbankcomputing.com/software/sip/intro) sources only. Doxygen +# will parse them like normal C++ but will assume all classes use public instead +# of private inheritance when no explicit protection keyword is present. +# The default value is: NO. + +SIP_SUPPORT = NO + +# For Microsoft's IDL there are propget and propput attributes to indicate +# getter and setter methods for a property. Setting this option to YES will make +# doxygen to replace the get and set methods by a property in the documentation. +# This will only work if the methods are indeed getting or setting a simple +# type. If this is not the case, or you want to show the methods anyway, you +# should set this option to NO. +# The default value is: YES. + +IDL_PROPERTY_SUPPORT = YES + +# If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC +# tag is set to YES then doxygen will reuse the documentation of the first +# member in the group (if any) for the other members of the group. By default +# all members of a group must be documented explicitly. +# The default value is: NO. + +DISTRIBUTE_GROUP_DOC = NO + +# If one adds a struct or class to a group and this option is enabled, then also +# any nested class or struct is added to the same group. By default this option +# is disabled and one has to add nested compounds explicitly via \ingroup. +# The default value is: NO. + +GROUP_NESTED_COMPOUNDS = NO + +# Set the SUBGROUPING tag to YES to allow class member groups of the same type +# (for instance a group of public functions) to be put as a subgroup of that +# type (e.g. under the Public Functions section). Set it to NO to prevent +# subgrouping. Alternatively, this can be done per class using the +# \nosubgrouping command. +# The default value is: YES. + +SUBGROUPING = YES + +# When the INLINE_GROUPED_CLASSES tag is set to YES, classes, structs and unions +# are shown inside the group in which they are included (e.g. using \ingroup) +# instead of on a separate page (for HTML and Man pages) or section (for LaTeX +# and RTF). +# +# Note that this feature does not work in combination with +# SEPARATE_MEMBER_PAGES. +# The default value is: NO. + +INLINE_GROUPED_CLASSES = NO + +# When the INLINE_SIMPLE_STRUCTS tag is set to YES, structs, classes, and unions +# with only public data fields or simple typedef fields will be shown inline in +# the documentation of the scope in which they are defined (i.e. file, +# namespace, or group documentation), provided this scope is documented. If set +# to NO, structs, classes, and unions are shown on a separate page (for HTML and +# Man pages) or section (for LaTeX and RTF). +# The default value is: NO. + +INLINE_SIMPLE_STRUCTS = NO + +# When TYPEDEF_HIDES_STRUCT tag is enabled, a typedef of a struct, union, or +# enum is documented as struct, union, or enum with the name of the typedef. So +# typedef struct TypeS {} TypeT, will appear in the documentation as a struct +# with name TypeT. When disabled the typedef will appear as a member of a file, +# namespace, or class. And the struct will be named TypeS. This can typically be +# useful for C code in case the coding convention dictates that all compound +# types are typedef'ed and only the typedef is referenced, never the tag name. +# The default value is: NO. + +TYPEDEF_HIDES_STRUCT = NO + +# The size of the symbol lookup cache can be set using LOOKUP_CACHE_SIZE. This +# cache is used to resolve symbols given their name and scope. Since this can be +# an expensive process and often the same symbol appears multiple times in the +# code, doxygen keeps a cache of pre-resolved symbols. If the cache is too small +# doxygen will become slower. If the cache is too large, memory is wasted. The +# cache size is given by this formula: 2^(16+LOOKUP_CACHE_SIZE). The valid range +# is 0..9, the default is 0, corresponding to a cache size of 2^16=65536 +# symbols. At the end of a run doxygen will report the cache usage and suggest +# the optimal cache size from a speed point of view. +# Minimum value: 0, maximum value: 9, default value: 0. + +LOOKUP_CACHE_SIZE = 0 + +#--------------------------------------------------------------------------- +# Build related configuration options +#--------------------------------------------------------------------------- + +# If the EXTRACT_ALL tag is set to YES, doxygen will assume all entities in +# documentation are documented, even if no documentation was available. Private +# class members and static file members will be hidden unless the +# EXTRACT_PRIVATE respectively EXTRACT_STATIC tags are set to YES. +# Note: This will also disable the warnings about undocumented members that are +# normally produced when WARNINGS is set to YES. +# The default value is: NO. + +EXTRACT_ALL = NO + +# If the EXTRACT_PRIVATE tag is set to YES, all private members of a class will +# be included in the documentation. +# The default value is: NO. + +EXTRACT_PRIVATE = NO + +# If the EXTRACT_PACKAGE tag is set to YES, all members with package or internal +# scope will be included in the documentation. +# The default value is: NO. + +EXTRACT_PACKAGE = NO + +# If the EXTRACT_STATIC tag is set to YES, all static members of a file will be +# included in the documentation. +# The default value is: NO. + +EXTRACT_STATIC = NO + +# If the EXTRACT_LOCAL_CLASSES tag is set to YES, classes (and structs) defined +# locally in source files will be included in the documentation. If set to NO, +# only classes defined in header files are included. Does not have any effect +# for Java sources. +# The default value is: YES. + +EXTRACT_LOCAL_CLASSES = YES + +# This flag is only useful for Objective-C code. If set to YES, local methods, +# which are defined in the implementation section but not in the interface are +# included in the documentation. If set to NO, only methods in the interface are +# included. +# The default value is: NO. + +EXTRACT_LOCAL_METHODS = NO + +# If this flag is set to YES, the members of anonymous namespaces will be +# extracted and appear in the documentation as a namespace called +# 'anonymous_namespace{file}', where file will be replaced with the base name of +# the file that contains the anonymous namespace. By default anonymous namespace +# are hidden. +# The default value is: NO. + +EXTRACT_ANON_NSPACES = NO + +# If the HIDE_UNDOC_MEMBERS tag is set to YES, doxygen will hide all +# undocumented members inside documented classes or files. If set to NO these +# members will be included in the various overviews, but no documentation +# section is generated. This option has no effect if EXTRACT_ALL is enabled. +# The default value is: NO. + +HIDE_UNDOC_MEMBERS = NO + +# If the HIDE_UNDOC_CLASSES tag is set to YES, doxygen will hide all +# undocumented classes that are normally visible in the class hierarchy. If set +# to NO, these classes will be included in the various overviews. This option +# has no effect if EXTRACT_ALL is enabled. +# The default value is: NO. + +HIDE_UNDOC_CLASSES = NO + +# If the HIDE_FRIEND_COMPOUNDS tag is set to YES, doxygen will hide all friend +# (class|struct|union) declarations. If set to NO, these declarations will be +# included in the documentation. +# The default value is: NO. + +HIDE_FRIEND_COMPOUNDS = NO + +# If the HIDE_IN_BODY_DOCS tag is set to YES, doxygen will hide any +# documentation blocks found inside the body of a function. If set to NO, these +# blocks will be appended to the function's detailed documentation block. +# The default value is: NO. + +HIDE_IN_BODY_DOCS = NO + +# The INTERNAL_DOCS tag determines if documentation that is typed after a +# \internal command is included. If the tag is set to NO then the documentation +# will be excluded. Set it to YES to include the internal documentation. +# The default value is: NO. + +INTERNAL_DOCS = NO + +# If the CASE_SENSE_NAMES tag is set to NO then doxygen will only generate file +# names in lower-case letters. If set to YES, upper-case letters are also +# allowed. This is useful if you have classes or files whose names only differ +# in case and if your file system supports case sensitive file names. Windows +# and Mac users are advised to set this option to NO. +# The default value is: system dependent. + +CASE_SENSE_NAMES = NO + +# If the HIDE_SCOPE_NAMES tag is set to NO then doxygen will show members with +# their full class and namespace scopes in the documentation. If set to YES, the +# scope will be hidden. +# The default value is: NO. + +HIDE_SCOPE_NAMES = NO + +# If the HIDE_COMPOUND_REFERENCE tag is set to NO (default) then doxygen will +# append additional text to a page's title, such as Class Reference. If set to +# YES the compound reference will be hidden. +# The default value is: NO. + +HIDE_COMPOUND_REFERENCE= NO + +# If the SHOW_INCLUDE_FILES tag is set to YES then doxygen will put a list of +# the files that are included by a file in the documentation of that file. +# The default value is: YES. + +SHOW_INCLUDE_FILES = YES + +# If the SHOW_GROUPED_MEMB_INC tag is set to YES then Doxygen will add for each +# grouped member an include statement to the documentation, telling the reader +# which file to include in order to use the member. +# The default value is: NO. + +SHOW_GROUPED_MEMB_INC = NO + +# If the FORCE_LOCAL_INCLUDES tag is set to YES then doxygen will list include +# files with double quotes in the documentation rather than with sharp brackets. +# The default value is: NO. + +FORCE_LOCAL_INCLUDES = NO + +# If the INLINE_INFO tag is set to YES then a tag [inline] is inserted in the +# documentation for inline members. +# The default value is: YES. + +INLINE_INFO = YES + +# If the SORT_MEMBER_DOCS tag is set to YES then doxygen will sort the +# (detailed) documentation of file and class members alphabetically by member +# name. If set to NO, the members will appear in declaration order. +# The default value is: YES. + +SORT_MEMBER_DOCS = YES + +# If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the brief +# descriptions of file, namespace and class members alphabetically by member +# name. If set to NO, the members will appear in declaration order. Note that +# this will also influence the order of the classes in the class list. +# The default value is: NO. + +SORT_BRIEF_DOCS = NO + +# If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the +# (brief and detailed) documentation of class members so that constructors and +# destructors are listed first. If set to NO the constructors will appear in the +# respective orders defined by SORT_BRIEF_DOCS and SORT_MEMBER_DOCS. +# Note: If SORT_BRIEF_DOCS is set to NO this option is ignored for sorting brief +# member documentation. +# Note: If SORT_MEMBER_DOCS is set to NO this option is ignored for sorting +# detailed member documentation. +# The default value is: NO. + +SORT_MEMBERS_CTORS_1ST = NO + +# If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the hierarchy +# of group names into alphabetical order. If set to NO the group names will +# appear in their defined order. +# The default value is: NO. + +SORT_GROUP_NAMES = NO + +# If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be sorted by +# fully-qualified names, including namespaces. If set to NO, the class list will +# be sorted only by class name, not including the namespace part. +# Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. +# Note: This option applies only to the class list, not to the alphabetical +# list. +# The default value is: NO. + +SORT_BY_SCOPE_NAME = NO + +# If the STRICT_PROTO_MATCHING option is enabled and doxygen fails to do proper +# type resolution of all parameters of a function it will reject a match between +# the prototype and the implementation of a member function even if there is +# only one candidate or it is obvious which candidate to choose by doing a +# simple string match. By disabling STRICT_PROTO_MATCHING doxygen will still +# accept a match between prototype and implementation in such cases. +# The default value is: NO. + +STRICT_PROTO_MATCHING = NO + +# The GENERATE_TODOLIST tag can be used to enable (YES) or disable (NO) the todo +# list. This list is created by putting \todo commands in the documentation. +# The default value is: YES. + +GENERATE_TODOLIST = YES + +# The GENERATE_TESTLIST tag can be used to enable (YES) or disable (NO) the test +# list. This list is created by putting \test commands in the documentation. +# The default value is: YES. + +GENERATE_TESTLIST = YES + +# The GENERATE_BUGLIST tag can be used to enable (YES) or disable (NO) the bug +# list. This list is created by putting \bug commands in the documentation. +# The default value is: YES. + +GENERATE_BUGLIST = YES + +# The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or disable (NO) +# the deprecated list. This list is created by putting \deprecated commands in +# the documentation. +# The default value is: YES. + +GENERATE_DEPRECATEDLIST= YES + +# The ENABLED_SECTIONS tag can be used to enable conditional documentation +# sections, marked by \if ... \endif and \cond +# ... \endcond blocks. + +ENABLED_SECTIONS = + +# The MAX_INITIALIZER_LINES tag determines the maximum number of lines that the +# initial value of a variable or macro / define can have for it to appear in the +# documentation. If the initializer consists of more lines than specified here +# it will be hidden. Use a value of 0 to hide initializers completely. The +# appearance of the value of individual variables and macros / defines can be +# controlled using \showinitializer or \hideinitializer command in the +# documentation regardless of this setting. +# Minimum value: 0, maximum value: 10000, default value: 30. + +MAX_INITIALIZER_LINES = 30 + +# Set the SHOW_USED_FILES tag to NO to disable the list of files generated at +# the bottom of the documentation of classes and structs. If set to YES, the +# list will mention the files that were used to generate the documentation. +# The default value is: YES. + +SHOW_USED_FILES = YES + +# Set the SHOW_FILES tag to NO to disable the generation of the Files page. This +# will remove the Files entry from the Quick Index and from the Folder Tree View +# (if specified). +# The default value is: YES. + +SHOW_FILES = YES + +# Set the SHOW_NAMESPACES tag to NO to disable the generation of the Namespaces +# page. This will remove the Namespaces entry from the Quick Index and from the +# Folder Tree View (if specified). +# The default value is: YES. + +SHOW_NAMESPACES = YES + +# The FILE_VERSION_FILTER tag can be used to specify a program or script that +# doxygen should invoke to get the current version for each file (typically from +# the version control system). Doxygen will invoke the program by executing (via +# popen()) the command command input-file, where command is the value of the +# FILE_VERSION_FILTER tag, and input-file is the name of an input file provided +# by doxygen. Whatever the program writes to standard output is used as the file +# version. For an example see the documentation. + +FILE_VERSION_FILTER = + +# The LAYOUT_FILE tag can be used to specify a layout file which will be parsed +# by doxygen. The layout file controls the global structure of the generated +# output files in an output format independent way. To create the layout file +# that represents doxygen's defaults, run doxygen with the -l option. You can +# optionally specify a file name after the option, if omitted DoxygenLayout.xml +# will be used as the name of the layout file. +# +# Note that if you run doxygen from a directory containing a file called +# DoxygenLayout.xml, doxygen will parse it automatically even if the LAYOUT_FILE +# tag is left empty. + +LAYOUT_FILE = + +# The CITE_BIB_FILES tag can be used to specify one or more bib files containing +# the reference definitions. This must be a list of .bib files. The .bib +# extension is automatically appended if omitted. This requires the bibtex tool +# to be installed. See also https://en.wikipedia.org/wiki/BibTeX for more info. +# For LaTeX the style of the bibliography can be controlled using +# LATEX_BIB_STYLE. To use this feature you need bibtex and perl available in the +# search path. See also \cite for info how to create references. + +CITE_BIB_FILES = + +#--------------------------------------------------------------------------- +# Configuration options related to warning and progress messages +#--------------------------------------------------------------------------- + +# The QUIET tag can be used to turn on/off the messages that are generated to +# standard output by doxygen. If QUIET is set to YES this implies that the +# messages are off. +# The default value is: NO. + +QUIET = NO + +# The WARNINGS tag can be used to turn on/off the warning messages that are +# generated to standard error (stderr) by doxygen. If WARNINGS is set to YES +# this implies that the warnings are on. +# +# Tip: Turn warnings on while writing the documentation. +# The default value is: YES. + +WARNINGS = YES + +# If the WARN_IF_UNDOCUMENTED tag is set to YES then doxygen will generate +# warnings for undocumented members. If EXTRACT_ALL is set to YES then this flag +# will automatically be disabled. +# The default value is: YES. + +WARN_IF_UNDOCUMENTED = YES + +# If the WARN_IF_DOC_ERROR tag is set to YES, doxygen will generate warnings for +# potential errors in the documentation, such as not documenting some parameters +# in a documented function, or documenting parameters that don't exist or using +# markup commands wrongly. +# The default value is: YES. + +WARN_IF_DOC_ERROR = YES + +# This WARN_NO_PARAMDOC option can be enabled to get warnings for functions that +# are documented, but have no documentation for their parameters or return +# value. If set to NO, doxygen will only warn about wrong or incomplete +# parameter documentation, but not about the absence of documentation. +# The default value is: NO. + +WARN_NO_PARAMDOC = NO + +# If the WARN_AS_ERROR tag is set to YES then doxygen will immediately stop when +# a warning is encountered. +# The default value is: NO. + +WARN_AS_ERROR = NO + +# The WARN_FORMAT tag determines the format of the warning messages that doxygen +# can produce. The string should contain the $file, $line, and $text tags, which +# will be replaced by the file and line number from which the warning originated +# and the warning text. Optionally the format may contain $version, which will +# be replaced by the version of the file (if it could be obtained via +# FILE_VERSION_FILTER) +# The default value is: $file:$line: $text. + +WARN_FORMAT = "$file:$line: $text" + +# The WARN_LOGFILE tag can be used to specify a file to which warning and error +# messages should be written. If left blank the output is written to standard +# error (stderr). + +WARN_LOGFILE = + +#--------------------------------------------------------------------------- +# Configuration options related to the input files +#--------------------------------------------------------------------------- + +# If the value of the INPUT tag contains directories, you can use the +# FILE_PATTERNS tag to specify one or more wildcard patterns (like *.cpp and +# *.h) to filter out the source-files in the directories. +# +# Note that for custom extensions or not directly supported extensions you also +# need to set EXTENSION_MAPPING for the extension otherwise the files are not +# read by doxygen. +# +# If left blank the following patterns are tested:*.c, *.cc, *.cxx, *.cpp, +# *.c++, *.java, *.ii, *.ixx, *.ipp, *.i++, *.inl, *.idl, *.ddl, *.odl, *.h, +# *.hh, *.hxx, *.hpp, *.h++, *.cs, *.d, *.php, *.php4, *.php5, *.phtml, *.inc, +# *.m, *.markdown, *.md, *.mm, *.dox, *.py, *.pyw, *.f90, *.f95, *.f03, *.f08, +# *.f, *.for, *.tcl, *.vhd, *.vhdl, *.ucf and *.qsf. + +FILE_PATTERNS = *.c \ + *.cc \ + *.cxx \ + *.cpp \ + *.c++ \ + *.java \ + *.ii \ + *.ixx \ + *.ipp \ + *.i++ \ + *.inl \ + *.idl \ + *.ddl \ + *.odl \ + *.h \ + *.hh \ + *.hxx \ + *.hpp \ + *.h++ \ + *.cs \ + *.d \ + *.php \ + *.php4 \ + *.php5 \ + *.phtml \ + *.inc \ + *.m \ + *.markdown \ + *.md \ + *.mm \ + *.dox \ + *.py \ + *.pyw \ + *.f90 \ + *.f95 \ + *.f03 \ + *.f08 \ + *.f \ + *.for \ + *.tcl \ + *.vhd \ + *.vhdl \ + *.ucf \ + *.qsf + +# The RECURSIVE tag can be used to specify whether or not subdirectories should +# be searched for input files as well. +# The default value is: NO. + +RECURSIVE = NO + +# The EXCLUDE tag can be used to specify files and/or directories that should be +# excluded from the INPUT source files. This way you can easily exclude a +# subdirectory from a directory tree whose root is specified with the INPUT tag. +# +# Note that relative paths are relative to the directory from which doxygen is +# run. + +EXCLUDE = + +# The EXCLUDE_SYMLINKS tag can be used to select whether or not files or +# directories that are symbolic links (a Unix file system feature) are excluded +# from the input. +# The default value is: NO. + +EXCLUDE_SYMLINKS = NO + +# If the value of the INPUT tag contains directories, you can use the +# EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude +# certain files from those directories. +# +# Note that the wildcards are matched against the file with absolute path, so to +# exclude all test directories for example use the pattern */test/* + +EXCLUDE_PATTERNS = + +# The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names +# (namespaces, classes, functions, etc.) that should be excluded from the +# output. The symbol name can be a fully qualified name, a word, or if the +# wildcard * is used, a substring. Examples: ANamespace, AClass, +# AClass::ANamespace, ANamespace::*Test +# +# Note that the wildcards are matched against the file with absolute path, so to +# exclude all test directories use the pattern */test/* + +EXCLUDE_SYMBOLS = + +# The EXAMPLE_PATH tag can be used to specify one or more files or directories +# that contain example code fragments that are included (see the \include +# command). + +EXAMPLE_PATH = + +# If the value of the EXAMPLE_PATH tag contains directories, you can use the +# EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp and +# *.h) to filter out the source-files in the directories. If left blank all +# files are included. + +EXAMPLE_PATTERNS = * + +# If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be +# searched for input files to be used with the \include or \dontinclude commands +# irrespective of the value of the RECURSIVE tag. +# The default value is: NO. + +EXAMPLE_RECURSIVE = NO + +# The IMAGE_PATH tag can be used to specify one or more files or directories +# that contain images that are to be included in the documentation (see the +# \image command). + +IMAGE_PATH = + +# The INPUT_FILTER tag can be used to specify a program that doxygen should +# invoke to filter for each input file. Doxygen will invoke the filter program +# by executing (via popen()) the command: +# +# +# +# where is the value of the INPUT_FILTER tag, and is the +# name of an input file. Doxygen will then use the output that the filter +# program writes to standard output. If FILTER_PATTERNS is specified, this tag +# will be ignored. +# +# Note that the filter must not add or remove lines; it is applied before the +# code is scanned, but not when the output code is generated. If lines are added +# or removed, the anchors will not be placed correctly. +# +# Note that for custom extensions or not directly supported extensions you also +# need to set EXTENSION_MAPPING for the extension otherwise the files are not +# properly processed by doxygen. + +INPUT_FILTER = + +# The FILTER_PATTERNS tag can be used to specify filters on a per file pattern +# basis. Doxygen will compare the file name with each pattern and apply the +# filter if there is a match. The filters are a list of the form: pattern=filter +# (like *.cpp=my_cpp_filter). See INPUT_FILTER for further information on how +# filters are used. If the FILTER_PATTERNS tag is empty or if none of the +# patterns match the file name, INPUT_FILTER is applied. +# +# Note that for custom extensions or not directly supported extensions you also +# need to set EXTENSION_MAPPING for the extension otherwise the files are not +# properly processed by doxygen. + +FILTER_PATTERNS = + +# If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using +# INPUT_FILTER) will also be used to filter the input files that are used for +# producing the source files to browse (i.e. when SOURCE_BROWSER is set to YES). +# The default value is: NO. + +FILTER_SOURCE_FILES = NO + +# The FILTER_SOURCE_PATTERNS tag can be used to specify source filters per file +# pattern. A pattern will override the setting for FILTER_PATTERN (if any) and +# it is also possible to disable source filtering for a specific pattern using +# *.ext= (so without naming a filter). +# This tag requires that the tag FILTER_SOURCE_FILES is set to YES. + +FILTER_SOURCE_PATTERNS = + +# If the USE_MDFILE_AS_MAINPAGE tag refers to the name of a markdown file that +# is part of the input, its contents will be placed on the main page +# (index.html). This can be useful if you have a project on for instance GitHub +# and want to reuse the introduction page also for the doxygen output. + +USE_MDFILE_AS_MAINPAGE = + +#--------------------------------------------------------------------------- +# Configuration options related to source browsing +#--------------------------------------------------------------------------- + +# If the SOURCE_BROWSER tag is set to YES then a list of source files will be +# generated. Documented entities will be cross-referenced with these sources. +# +# Note: To get rid of all source code in the generated output, make sure that +# also VERBATIM_HEADERS is set to NO. +# The default value is: NO. + +SOURCE_BROWSER = YES + +# Setting the INLINE_SOURCES tag to YES will include the body of functions, +# classes and enums directly into the documentation. +# The default value is: NO. + +INLINE_SOURCES = NO + +# Setting the STRIP_CODE_COMMENTS tag to YES will instruct doxygen to hide any +# special comment blocks from generated source code fragments. Normal C, C++ and +# Fortran comments will always remain visible. +# The default value is: YES. + +STRIP_CODE_COMMENTS = YES + +# If the REFERENCED_BY_RELATION tag is set to YES then for each documented +# function all documented functions referencing it will be listed. +# The default value is: NO. + +REFERENCED_BY_RELATION = NO + +# If the REFERENCES_RELATION tag is set to YES then for each documented function +# all documented entities called/used by that function will be listed. +# The default value is: NO. + +REFERENCES_RELATION = NO + +# If the REFERENCES_LINK_SOURCE tag is set to YES and SOURCE_BROWSER tag is set +# to YES then the hyperlinks from functions in REFERENCES_RELATION and +# REFERENCED_BY_RELATION lists will link to the source code. Otherwise they will +# link to the documentation. +# The default value is: YES. + +REFERENCES_LINK_SOURCE = YES + +# If SOURCE_TOOLTIPS is enabled (the default) then hovering a hyperlink in the +# source code will show a tooltip with additional information such as prototype, +# brief description and links to the definition and documentation. Since this +# will make the HTML file larger and loading of large files a bit slower, you +# can opt to disable this feature. +# The default value is: YES. +# This tag requires that the tag SOURCE_BROWSER is set to YES. + +SOURCE_TOOLTIPS = YES + +# If the USE_HTAGS tag is set to YES then the references to source code will +# point to the HTML generated by the htags(1) tool instead of doxygen built-in +# source browser. The htags tool is part of GNU's global source tagging system +# (see https://www.gnu.org/software/global/global.html). You will need version +# 4.8.6 or higher. +# +# To use it do the following: +# - Install the latest version of global +# - Enable SOURCE_BROWSER and USE_HTAGS in the config file +# - Make sure the INPUT points to the root of the source tree +# - Run doxygen as normal +# +# Doxygen will invoke htags (and that will in turn invoke gtags), so these +# tools must be available from the command line (i.e. in the search path). +# +# The result: instead of the source browser generated by doxygen, the links to +# source code will now point to the output of htags. +# The default value is: NO. +# This tag requires that the tag SOURCE_BROWSER is set to YES. + +USE_HTAGS = NO + +# If the VERBATIM_HEADERS tag is set the YES then doxygen will generate a +# verbatim copy of the header file for each class for which an include is +# specified. Set to NO to disable this. +# See also: Section \class. +# The default value is: YES. + +VERBATIM_HEADERS = YES + +#--------------------------------------------------------------------------- +# Configuration options related to the alphabetical class index +#--------------------------------------------------------------------------- + +# If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index of all +# compounds will be generated. Enable this if the project contains a lot of +# classes, structs, unions or interfaces. +# The default value is: YES. + +ALPHABETICAL_INDEX = YES + +# The COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns in +# which the alphabetical index list will be split. +# Minimum value: 1, maximum value: 20, default value: 5. +# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. + +COLS_IN_ALPHA_INDEX = 5 + +# In case all classes in a project start with a common prefix, all classes will +# be put under the same header in the alphabetical index. The IGNORE_PREFIX tag +# can be used to specify a prefix (or a list of prefixes) that should be ignored +# while generating the index headers. +# This tag requires that the tag ALPHABETICAL_INDEX is set to YES. + +IGNORE_PREFIX = + +#--------------------------------------------------------------------------- +# Configuration options related to the HTML output +#--------------------------------------------------------------------------- + +# If the GENERATE_HTML tag is set to YES, doxygen will generate HTML output +# The default value is: YES. + +GENERATE_HTML = YES + +# The HTML_OUTPUT tag is used to specify where the HTML docs will be put. If a +# relative path is entered the value of OUTPUT_DIRECTORY will be put in front of +# it. +# The default directory is: html. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_OUTPUT = html + +# The HTML_FILE_EXTENSION tag can be used to specify the file extension for each +# generated HTML page (for example: .htm, .php, .asp). +# The default value is: .html. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_FILE_EXTENSION = .html + +# The HTML_HEADER tag can be used to specify a user-defined HTML header file for +# each generated HTML page. If the tag is left blank doxygen will generate a +# standard header. +# +# To get valid HTML the header file that includes any scripts and style sheets +# that doxygen needs, which is dependent on the configuration options used (e.g. +# the setting GENERATE_TREEVIEW). It is highly recommended to start with a +# default header using +# doxygen -w html new_header.html new_footer.html new_stylesheet.css +# YourConfigFile +# and then modify the file new_header.html. See also section "Doxygen usage" +# for information on how to generate the default header that doxygen normally +# uses. +# Note: The header is subject to change so you typically have to regenerate the +# default header when upgrading to a newer version of doxygen. For a description +# of the possible markers and block names see the documentation. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_HEADER = + +# The HTML_FOOTER tag can be used to specify a user-defined HTML footer for each +# generated HTML page. If the tag is left blank doxygen will generate a standard +# footer. See HTML_HEADER for more information on how to generate a default +# footer and what special commands can be used inside the footer. See also +# section "Doxygen usage" for information on how to generate the default footer +# that doxygen normally uses. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_FOOTER = + +# The HTML_STYLESHEET tag can be used to specify a user-defined cascading style +# sheet that is used by each HTML page. It can be used to fine-tune the look of +# the HTML output. If left blank doxygen will generate a default style sheet. +# See also section "Doxygen usage" for information on how to generate the style +# sheet that doxygen normally uses. +# Note: It is recommended to use HTML_EXTRA_STYLESHEET instead of this tag, as +# it is more robust and this tag (HTML_STYLESHEET) will in the future become +# obsolete. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_STYLESHEET = + +# The HTML_EXTRA_STYLESHEET tag can be used to specify additional user-defined +# cascading style sheets that are included after the standard style sheets +# created by doxygen. Using this option one can overrule certain style aspects. +# This is preferred over using HTML_STYLESHEET since it does not replace the +# standard style sheet and is therefore more robust against future updates. +# Doxygen will copy the style sheet files to the output directory. +# Note: The order of the extra style sheet files is of importance (e.g. the last +# style sheet in the list overrules the setting of the previous ones in the +# list). For an example see the documentation. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_EXTRA_STYLESHEET = + +# The HTML_EXTRA_FILES tag can be used to specify one or more extra images or +# other source files which should be copied to the HTML output directory. Note +# that these files will be copied to the base HTML output directory. Use the +# $relpath^ marker in the HTML_HEADER and/or HTML_FOOTER files to load these +# files. In the HTML_STYLESHEET file, use the file name only. Also note that the +# files will be copied as-is; there are no commands or markers available. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_EXTRA_FILES = + +# The HTML_COLORSTYLE_HUE tag controls the color of the HTML output. Doxygen +# will adjust the colors in the style sheet and background images according to +# this color. Hue is specified as an angle on a colorwheel, see +# https://en.wikipedia.org/wiki/Hue for more information. For instance the value +# 0 represents red, 60 is yellow, 120 is green, 180 is cyan, 240 is blue, 300 +# purple, and 360 is red again. +# Minimum value: 0, maximum value: 359, default value: 220. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE_HUE = 220 + +# The HTML_COLORSTYLE_SAT tag controls the purity (or saturation) of the colors +# in the HTML output. For a value of 0 the output will use grayscales only. A +# value of 255 will produce the most vivid colors. +# Minimum value: 0, maximum value: 255, default value: 100. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE_SAT = 100 + +# The HTML_COLORSTYLE_GAMMA tag controls the gamma correction applied to the +# luminance component of the colors in the HTML output. Values below 100 +# gradually make the output lighter, whereas values above 100 make the output +# darker. The value divided by 100 is the actual gamma applied, so 80 represents +# a gamma of 0.8, The value 220 represents a gamma of 2.2, and 100 does not +# change the gamma. +# Minimum value: 40, maximum value: 240, default value: 80. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_COLORSTYLE_GAMMA = 80 + +# If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML +# page will contain the date and time when the page was generated. Setting this +# to YES can help to show when doxygen was last run and thus if the +# documentation is up to date. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_TIMESTAMP = NO + +# If the HTML_DYNAMIC_MENUS tag is set to YES then the generated HTML +# documentation will contain a main index with vertical navigation menus that +# are dynamically created via Javascript. If disabled, the navigation index will +# consists of multiple levels of tabs that are statically embedded in every HTML +# page. Disable this option to support browsers that do not have Javascript, +# like the Qt help browser. +# The default value is: YES. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_DYNAMIC_MENUS = YES + +# If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML +# documentation will contain sections that can be hidden and shown after the +# page has loaded. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_DYNAMIC_SECTIONS = NO + +# With HTML_INDEX_NUM_ENTRIES one can control the preferred number of entries +# shown in the various tree structured indices initially; the user can expand +# and collapse entries dynamically later on. Doxygen will expand the tree to +# such a level that at most the specified number of entries are visible (unless +# a fully collapsed tree already exceeds this amount). So setting the number of +# entries 1 will produce a full collapsed tree by default. 0 is a special value +# representing an infinite number of entries and will result in a full expanded +# tree by default. +# Minimum value: 0, maximum value: 9999, default value: 100. +# This tag requires that the tag GENERATE_HTML is set to YES. + +HTML_INDEX_NUM_ENTRIES = 100 + +# If the GENERATE_DOCSET tag is set to YES, additional index files will be +# generated that can be used as input for Apple's Xcode 3 integrated development +# environment (see: https://developer.apple.com/tools/xcode/), introduced with +# OSX 10.5 (Leopard). To create a documentation set, doxygen will generate a +# Makefile in the HTML output directory. Running make will produce the docset in +# that directory and running make install will install the docset in +# ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find it at +# startup. See https://developer.apple.com/tools/creatingdocsetswithdoxygen.html +# for more information. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_DOCSET = NO + +# This tag determines the name of the docset feed. A documentation feed provides +# an umbrella under which multiple documentation sets from a single provider +# (such as a company or product suite) can be grouped. +# The default value is: Doxygen generated docs. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_FEEDNAME = "Doxygen generated docs" + +# This tag specifies a string that should uniquely identify the documentation +# set bundle. This should be a reverse domain-name style string, e.g. +# com.mycompany.MyDocSet. Doxygen will append .docset to the name. +# The default value is: org.doxygen.Project. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_BUNDLE_ID = org.doxygen.Project + +# The DOCSET_PUBLISHER_ID tag specifies a string that should uniquely identify +# the documentation publisher. This should be a reverse domain-name style +# string, e.g. com.mycompany.MyDocSet.documentation. +# The default value is: org.doxygen.Publisher. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_PUBLISHER_ID = org.doxygen.Publisher + +# The DOCSET_PUBLISHER_NAME tag identifies the documentation publisher. +# The default value is: Publisher. +# This tag requires that the tag GENERATE_DOCSET is set to YES. + +DOCSET_PUBLISHER_NAME = Publisher + +# If the GENERATE_HTMLHELP tag is set to YES then doxygen generates three +# additional HTML index files: index.hhp, index.hhc, and index.hhk. The +# index.hhp is a project file that can be read by Microsoft's HTML Help Workshop +# (see: http://www.microsoft.com/en-us/download/details.aspx?id=21138) on +# Windows. +# +# The HTML Help Workshop contains a compiler that can convert all HTML output +# generated by doxygen into a single compiled HTML file (.chm). Compiled HTML +# files are now used as the Windows 98 help format, and will replace the old +# Windows help format (.hlp) on all Windows platforms in the future. Compressed +# HTML files also contain an index, a table of contents, and you can search for +# words in the documentation. The HTML workshop also contains a viewer for +# compressed HTML files. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_HTMLHELP = NO + +# The CHM_FILE tag can be used to specify the file name of the resulting .chm +# file. You can add a path in front of the file if the result should not be +# written to the html output directory. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +CHM_FILE = + +# The HHC_LOCATION tag can be used to specify the location (absolute path +# including file name) of the HTML help compiler (hhc.exe). If non-empty, +# doxygen will try to run the HTML help compiler on the generated index.hhp. +# The file has to be specified with full path. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +HHC_LOCATION = + +# The GENERATE_CHI flag controls if a separate .chi index file is generated +# (YES) or that it should be included in the master .chm file (NO). +# The default value is: NO. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +GENERATE_CHI = NO + +# The CHM_INDEX_ENCODING is used to encode HtmlHelp index (hhk), content (hhc) +# and project file content. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +CHM_INDEX_ENCODING = + +# The BINARY_TOC flag controls whether a binary table of contents is generated +# (YES) or a normal table of contents (NO) in the .chm file. Furthermore it +# enables the Previous and Next buttons. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +BINARY_TOC = NO + +# The TOC_EXPAND flag can be set to YES to add extra items for group members to +# the table of contents of the HTML help documentation and to the tree view. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTMLHELP is set to YES. + +TOC_EXPAND = NO + +# If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and +# QHP_VIRTUAL_FOLDER are set, an additional index file will be generated that +# can be used as input for Qt's qhelpgenerator to generate a Qt Compressed Help +# (.qch) of the generated HTML documentation. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_QHP = NO + +# If the QHG_LOCATION tag is specified, the QCH_FILE tag can be used to specify +# the file name of the resulting .qch file. The path specified is relative to +# the HTML output folder. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QCH_FILE = + +# The QHP_NAMESPACE tag specifies the namespace to use when generating Qt Help +# Project output. For more information please see Qt Help Project / Namespace +# (see: http://doc.qt.io/qt-4.8/qthelpproject.html#namespace). +# The default value is: org.doxygen.Project. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_NAMESPACE = org.doxygen.Project + +# The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating Qt +# Help Project output. For more information please see Qt Help Project / Virtual +# Folders (see: http://doc.qt.io/qt-4.8/qthelpproject.html#virtual-folders). +# The default value is: doc. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_VIRTUAL_FOLDER = doc + +# If the QHP_CUST_FILTER_NAME tag is set, it specifies the name of a custom +# filter to add. For more information please see Qt Help Project / Custom +# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters). +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_CUST_FILTER_NAME = + +# The QHP_CUST_FILTER_ATTRS tag specifies the list of the attributes of the +# custom filter to add. For more information please see Qt Help Project / Custom +# Filters (see: http://doc.qt.io/qt-4.8/qthelpproject.html#custom-filters). +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_CUST_FILTER_ATTRS = + +# The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this +# project's filter section matches. Qt Help Project / Filter Attributes (see: +# http://doc.qt.io/qt-4.8/qthelpproject.html#filter-attributes). +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHP_SECT_FILTER_ATTRS = + +# The QHG_LOCATION tag can be used to specify the location of Qt's +# qhelpgenerator. If non-empty doxygen will try to run qhelpgenerator on the +# generated .qhp file. +# This tag requires that the tag GENERATE_QHP is set to YES. + +QHG_LOCATION = + +# If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files will be +# generated, together with the HTML files, they form an Eclipse help plugin. To +# install this plugin and make it available under the help contents menu in +# Eclipse, the contents of the directory containing the HTML and XML files needs +# to be copied into the plugins directory of eclipse. The name of the directory +# within the plugins directory should be the same as the ECLIPSE_DOC_ID value. +# After copying Eclipse needs to be restarted before the help appears. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_ECLIPSEHELP = NO + +# A unique identifier for the Eclipse help plugin. When installing the plugin +# the directory name containing the HTML and XML files should also have this +# name. Each documentation set should have its own identifier. +# The default value is: org.doxygen.Project. +# This tag requires that the tag GENERATE_ECLIPSEHELP is set to YES. + +ECLIPSE_DOC_ID = org.doxygen.Project + +# If you want full control over the layout of the generated HTML pages it might +# be necessary to disable the index and replace it with your own. The +# DISABLE_INDEX tag can be used to turn on/off the condensed index (tabs) at top +# of each HTML page. A value of NO enables the index and the value YES disables +# it. Since the tabs in the index contain the same information as the navigation +# tree, you can set this option to YES if you also set GENERATE_TREEVIEW to YES. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +DISABLE_INDEX = NO + +# The GENERATE_TREEVIEW tag is used to specify whether a tree-like index +# structure should be generated to display hierarchical information. If the tag +# value is set to YES, a side panel will be generated containing a tree-like +# index structure (just like the one that is generated for HTML Help). For this +# to work a browser that supports JavaScript, DHTML, CSS and frames is required +# (i.e. any modern browser). Windows users are probably better off using the +# HTML help feature. Via custom style sheets (see HTML_EXTRA_STYLESHEET) one can +# further fine-tune the look of the index. As an example, the default style +# sheet generated by doxygen has an example that shows how to put an image at +# the root of the tree instead of the PROJECT_NAME. Since the tree basically has +# the same information as the tab index, you could consider setting +# DISABLE_INDEX to YES when enabling this option. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +GENERATE_TREEVIEW = NO + +# The ENUM_VALUES_PER_LINE tag can be used to set the number of enum values that +# doxygen will group on one line in the generated HTML documentation. +# +# Note that a value of 0 will completely suppress the enum values from appearing +# in the overview section. +# Minimum value: 0, maximum value: 20, default value: 4. +# This tag requires that the tag GENERATE_HTML is set to YES. + +ENUM_VALUES_PER_LINE = 4 + +# If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be used +# to set the initial width (in pixels) of the frame in which the tree is shown. +# Minimum value: 0, maximum value: 1500, default value: 250. +# This tag requires that the tag GENERATE_HTML is set to YES. + +TREEVIEW_WIDTH = 250 + +# If the EXT_LINKS_IN_WINDOW option is set to YES, doxygen will open links to +# external symbols imported via tag files in a separate window. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +EXT_LINKS_IN_WINDOW = NO + +# Use this tag to change the font size of LaTeX formulas included as images in +# the HTML documentation. When you change the font size after a successful +# doxygen run you need to manually remove any form_*.png images from the HTML +# output directory to force them to be regenerated. +# Minimum value: 8, maximum value: 50, default value: 10. +# This tag requires that the tag GENERATE_HTML is set to YES. + +FORMULA_FONTSIZE = 10 + +# Use the FORMULA_TRANSPARENT tag to determine whether or not the images +# generated for formulas are transparent PNGs. Transparent PNGs are not +# supported properly for IE 6.0, but are supported on all modern browsers. +# +# Note that when changing this option you need to delete any form_*.png files in +# the HTML output directory before the changes have effect. +# The default value is: YES. +# This tag requires that the tag GENERATE_HTML is set to YES. + +FORMULA_TRANSPARENT = YES + +# Enable the USE_MATHJAX option to render LaTeX formulas using MathJax (see +# https://www.mathjax.org) which uses client side Javascript for the rendering +# instead of using pre-rendered bitmaps. Use this if you do not have LaTeX +# installed or if you want to formulas look prettier in the HTML output. When +# enabled you may also need to install MathJax separately and configure the path +# to it using the MATHJAX_RELPATH option. +# The default value is: NO. +# This tag requires that the tag GENERATE_HTML is set to YES. + +USE_MATHJAX = NO + +# When MathJax is enabled you can set the default output format to be used for +# the MathJax output. See the MathJax site (see: +# http://docs.mathjax.org/en/latest/output.html) for more details. +# Possible values are: HTML-CSS (which is slower, but has the best +# compatibility), NativeMML (i.e. MathML) and SVG. +# The default value is: HTML-CSS. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_FORMAT = HTML-CSS + +# When MathJax is enabled you need to specify the location relative to the HTML +# output directory using the MATHJAX_RELPATH option. The destination directory +# should contain the MathJax.js script. For instance, if the mathjax directory +# is located at the same level as the HTML output directory, then +# MATHJAX_RELPATH should be ../mathjax. The default value points to the MathJax +# Content Delivery Network so you can quickly see the result without installing +# MathJax. However, it is strongly recommended to install a local copy of +# MathJax from https://www.mathjax.org before deployment. +# The default value is: http://cdn.mathjax.org/mathjax/latest. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_RELPATH = http://cdn.mathjax.org/mathjax/latest + +# The MATHJAX_EXTENSIONS tag can be used to specify one or more MathJax +# extension names that should be enabled during MathJax rendering. For example +# MATHJAX_EXTENSIONS = TeX/AMSmath TeX/AMSsymbols +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_EXTENSIONS = + +# The MATHJAX_CODEFILE tag can be used to specify a file with javascript pieces +# of code that will be used on startup of the MathJax code. See the MathJax site +# (see: http://docs.mathjax.org/en/latest/output.html) for more details. For an +# example see the documentation. +# This tag requires that the tag USE_MATHJAX is set to YES. + +MATHJAX_CODEFILE = + +# When the SEARCHENGINE tag is enabled doxygen will generate a search box for +# the HTML output. The underlying search engine uses javascript and DHTML and +# should work on any modern browser. Note that when using HTML help +# (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET) +# there is already a search function so this one should typically be disabled. +# For large projects the javascript based search engine can be slow, then +# enabling SERVER_BASED_SEARCH may provide a better solution. It is possible to +# search using the keyboard; to jump to the search box use + S +# (what the is depends on the OS and browser, but it is typically +# , /