Estimation of tumor purity is a crucial step in understanding the complex copy number landscapes of heterogeneous tumor samples through next-generation sequencing data. A prominent problem in tumor purity estimation is how to disambiguate multiple compatible solutions, as distinct combinations of tumor purity and ploidy can fit the sequencing data equivalently well. Current methods "break the tie" relying on either prior information or numerical heuristics rather than comprehensively explaining the sequencing data itself. Such approaches will lead to fundamental bias when the ground truth significantly deviates from the prior information. Here we propose a full probabilistic model-based method PyLOH that leverage the cluster pattern of loss of heterozygosity observed in paired cancer sequencing data to disambiguate multiple compatible solutions. We also introduce a novel visualization method "BAF heat map" to to characterize the cluster pattern of LOH.
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Although not mandatory, Linux system is recommended.
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Python (2.7). Python 2.7.3 is recommended.
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Numpy(>=1.6.1). You can download the source of Numpy from here.
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Scipy(>=0.10). You can download the source of Scipy from here.
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Pysam(>=0.7). To install Pysam, you also need to install Cython first.
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matplotlib(>=1.2.0) is required to plot BAF heat map.
Altough not required by PyLOH, samtools can be useful for creating bam, bam index and fasta index files which are required by the pysam module of PyLOH.
Download the compressed source file PyLOH-*.tar.gz and do as follows:
$ tar -xzvf PyLOH-*.tar.gz
$ cd PyLOH-*
$ python setup.py install
If you prefer to install PyLOH other than the default directory, you can also use this command:
$ python setup.py install --prefix /home/yili/
There are also config/ and bin/ folders under PyLOH-*. The config/ folder contains example priors and the bin/ folder contains
useful utilities, such as the R code to run BICseq and the python script to
convert BICseq results to BED file. You can copy these two folders somewhere easily accessible.
PyLOH is composed of three modules:
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preprocess. Preprocess the reads aliments of paired normal-tumor samples in BAM format and produces the paired counts file, preprocessed segments file and preprocessed BAF heat map file as output. -
run_model. Take the paired counts file and preprocessed segments file as input, estimates tumor purity, the copy number and the allele type of each segment. -
postprocess. Take the preprocessed BAF heat map file as input and plots the BAF heat map for each segment as output.
The general workflow of PyLOH is this
This part of README is based on JoinSNVMix. To preprocess the paired cancer sequencing data, execute:
$ PyLOH.py preprocess REFERENCE_GENOME.fasta NORMAL.bam TUMOUR.bam BASENAME --segments_bed_file_name SEGMENTS.bed --min_depth 20 --min_base_qual 10 --min_map_qual 10 --process_num 10
REFERENCE_GENOME.fasta The path to the fasta file that the paired BAM files aligned to. Note that the index file should be generated for the reference genome. This can be done by running samtools as follows:
$ samtools faidx REFERENCE_GENOME.fasta
NORMAL.bam The BAM file for the normal sample. The BAM index file should be generated for this file and named NORMAL.bam.bai. This can be done by running
$ samtools index NORMAL.bam
TUMOUR.bam The bam file for the tumour sample. As for the normal this file needs to be indexed.
BASENAME The base name of preprocessed files to be created.
--segments_bed_file_name SEGMENTS.bed Use the genome segmentation stored in SEGMENTS.bed. If not provided, use 22 autosomes as the segmentaion. But using automatic segmentation algorithm is highly recommended, such as BICseq.
--min_depth 20 Minimum depth of 20 in both tumor and normal sample required to use a site in the analysis.
--min_base_qual 10 Remove bases with base quality lower than 10.
--min_map_qual 10 Remove bases with mapping quality lower than 10.
--process_num 10 Use 10 processes to launch the preprocess module.