ATAC pipeline

This pipeline performs peak calling for ATAC data (only cisTopic option is available at the moment)

Contents of Repo

• main.nf - the Nextflow pipeline that runs the whole pipeline
• modules/ - a collection pipeline's of processes for different components
  • modules/pycistopic/main.nf - a collection of processes for cisTopic component
• workflows/ - a collection pipeline's of workflows for different components
  • workflows/pycistopic/main.nf - a collection of processes for cisTopic component
• bin - a colection of python scripts
  • bin/colored_logger.py - a custom python logger with colored output
  • bin/make_pseudobulk.py - a script to make a pseubobulks from fragments file
  • bin/peak_calling.py - a script to call peaks for celltypes' pseudobulks
  • bin/infer_consensus.py - a script to infer consensus peaks from pseudobulks' peak calling results
  • bin/create_cistopic.py - script that creates cisTopic object from consensus .bed and fragments files
• nextflow.config - the configuration script that controls everything

Pipeline Arguments

• --sample_table: specify a path to .csv file with sample names and path to the CellRanger-arc output dir (see example below)
• --celltypes: specify a path .csv file with celltype annotation or a path to pseudobulk_peaks.csv file with selected celltypes for consensus peak calling
• --callPeaks: if specified creates pseudobulks for celltypes specified in --celltypes for samples in --sample_table
• --inferConsensus: if specified runs a consensus peak calling and outputs cisTopic object for each sample in --sample_table

Examples of use:

1. Perform peak calling

To run peak calling for each celltype you need to specify sample table (see examples/sample_table.csv) and celltype annotation (see examples/celltype_annotation.csv):

nextflow run main.nf --callPeaks --sample_table ./examples/sample_table.csv --celltypes ./examples/celltype_annotation.csv

This creates results directory with the following files:

results/
├── log
│   ├── fragments_celltype_x_sample.csv # fragment counts matrix with shape (n_celltypes, n_samples)
│   ├── fragments_per_celltype.csv # total fragment counts for each celltype
│   ├── pseudobulk.audiovisual_neuroepithelium.log
│   ├── pseudobulk.craniofacial.log
├── narrowPeaks
│   ├── audiovisual_neuroepithelium_peaks.narrowPeak
│   ├── craniofacial_peaks.narrowPeak
├── pseudobulk_peaks.csv # contains fragment, peak counts and path to .narrowPeak file for each celltype (see example/pseudobulk_peaks.csv)
└── updated_sample_table.csv # updated sample table which contains fragment counts (see example/updated_sample_table.csv)

2. Infer consensus peaks and calculate features

To run consensus peak calling and feature calculation you need to specify an updated sample table generated on previous step (it is essential to use updated table with fragment counts to set appropriate memory limits for jobs) and pseudobulk peaks table generated on previous step with selected celltypes:

nextflow run main.nf --inferConsensus --sample_table ./results/updated_sample_table.csv --celltypes ./results/pseudobulk_peaks.csv

This will create a consensus_paeks.bed file and cisTopic objects for each sample:

results
├── consensus_peaks.bed # consensus peaks
├── WS_wEMB13400228
│   ├── qc
│   └── WS_wEMB13400228_cistopic_obj.pkl
└── WS_wEMB13400229
    ├── qc
    └── WS_wEMB13400229_cistopic_obj.pkl

3. Perform peak calling, infer consensus peaks and calculate features

To run all steps together you can use the following command:

nextflow run main.nf --callPeaks --inferConsensus --sample_table ./examples/sample_table.csv --celltypes examples/celltype_annotation.csv