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Using the Tool - CNV Input

This section describes how to generate Copy Number Variation (CNV) matrices using SigProfilerMatrixGenerator.

CNV Quick Start Example

To generate a copy number matrix, provide an absolute path to a multi-sample segmentation file obtained from one of the supported copy number calling tools.

Note: If you have individual sample files, combine them into one file with the first column corresponding to the sample name.

Supported CNV Callers

  • ASCAT
  • ASCAT_NGS
  • SEQUENZA
  • ABSOLUTE
  • BATTENBERG
  • FACETS
  • PURPLE
  • TCGA

Example Input File (Battenberg)

sample chrom startpos endpos nMaj1_A nMin1_A frac_1A nMaj2_A nMin1_A frac_2A
NSLC-1060-T01 19 90407080 91002641 1 0 0.67 2 0 0.33

Note: For Battenberg input, information for both the clone and subclone is considered and counted as separate events if multiple subclones are present.

Python Usage

python3
>>> from SigProfilerMatrixGenerator.scripts import CNVMatrixGenerator as scna
>>> file_type = "BATTENBERG"
>>> input_file = "./SigProfilerMatrixGenerator/references/CNV/example_input/Battenberg_test.tsv"
>>> output_path = "/Users/user/CNVMatrixGenerator/example_output/"
>>> project = "Battenberg_test"
>>> scna.generateCNVMatrix(file_type, input_file, project, output_path)

Command Line Usage

python ./SigProfilerMatrixGenerator/scripts/CNVMatrixGenerator.py BATTENBERG ./SigProfilerMatrixGenerator/references/CNV/example_input/Battenberg_test.tsv BATTENBERG-TEST ./SigProfilerMatrixGenerator/references/CNV/example_output/

R Usage

conda create --name spmg_r -y
conda activate spmg_r
conda install python=3.10 r-base r-devtools r-reticulate -c conda-forge -y
pip install SigProfilerMatrixGenerator
echo 'devtools::install_github("SigProfilerSuite/SigProfilerMatrixGeneratorR")' | Rscript -

R Example

R
> library("reticulate")
> use_python("path_to_your_python3")
> py_config()
> library("SigProfilerMatrixGeneratorR")
> cnv <- CNVMatrixGenerator("BATTENBERG", "[your_repo]/test_data/CNV/Battenberg_test.tsv", "test_CNV", "[your_repo]/test_data/CNV")

Function Arguments

Required Parameters

Parameter Type Description Example
file_type string Segmentation/caller type "BATTENBERG"
input_file string Path to the segmentation file "./path/to/file.tsv"
project string Project name for this instance "my_project"
output_path string Path to directory for output files "./output/"

Supported File Types

["ASCAT", "ASCAT_NGS", "SEQUENZA", "ABSOLUTE", "BATTENBERG", "FACETS", "PURPLE", "TCGA"]

CNV Classification Schema

The copy number classification schema consists of 48 mutually exclusive channels, divided by:

  1. Heterozygosity status (Het vs LOH vs Homozygous Deletion)
  2. Segment size (0-100kb, 100kb-1Mb, 1Mb-10Mb, 10Mb-40Mb, >40Mb)
  3. Total Copy Number (TCN)

Heterozygous State

Both alleles are retained and either one or both can be amplified. - TCN categories: 1, 2, 3-4, 5-8, ≥9

Loss of Heterozygosity (LOH)

One allele is lost; the remaining allele can be duplicated. - TCN categories: 0, 1, 2, 3-4, 5-8, ≥9

Homozygous Deletions

Both alleles are lost. - Size categories: 0-100kb, 100kb-1Mb, >1Mb