Executing the Program

The main workflow is executed through pyEpiAneufinder.epiAneufinder(). When using a fragment file as input, the file should be ordered first by cell barcode and then by genomic position. pyEpiAneufinder can do this sorting internally when sort_fragment=True.

Alternatively, the fragment_file argument can point to a Cell Ranger-style matrix directory containing the peaks, barcodes, and matrix files. In that case, set cellRangerInput=True. Fragment-file input is still the preferred path when available.

By default, the workflow performs GC correction. The correction can be disabled with GC=False, but the default is recommended.

The workflow also needs:

• a reference genome FASTA file

• a blacklist BED file

• a window size

• an output directory

• optional chromosomes to exclude

• a minimum fragment cutoff for filtering cells

For example:

import pyEpiAneufinder as pea

pea.epiAneufinder(
    fragment_file="sample_data/sample.tsv.gz",
    outdir="results_sample_data",
    genome_file="hg38.fa.gz",
    blacklist="sample_data/hg38-blacklist.v2.bed",
    windowSize=100000,
    ncores=1,
    exclude=["chrX", "chrY"],
    minFrags=20000,
    resume=False,
    cellRangerInput=False,
    GC=True,
    sort_fragment=True,
    remove_barcodes=None,
)

The test dataset used in the examples is available in the repository under sample_data/.

The main output is written to results_sample_data/outs/result_table.tsv.gz. This table contains the consensus CNV state per cell and genomic bin, encoded as 0=loss, 0.5=weak loss, 1=base, 1.5=weak gain, and 2=gain. Additional intermediate outputs are described on the Input and Output page.

The example run produces the following karyogram: