Using the Tool - SV Input

This section describes how to generate Structural Variant (SV) matrices using SigProfilerMatrixGenerator.

Input Format

The first six columns are required, and either the column svclass OR the columns strand1 & strand2 must be present.

Example with SV Class (TSV or CSV file)

chrom1	start1	end1	chrom2	start2	end2	svclass
19	21268384	21268385	19	21327858	21327859	deletion

Example with Strand Information (TSV or CSV file)

chrom1	start1	end1	chrom2	start2	end2	strand1	strand2
19	21268384	21268385	19	21327858	21327859	+	+

SV Classes

• deletion
• translocation
• tandem-duplication
• inversion

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Python Usage

# Navigate to SVMatrixGenerator directory and start python3 interpreter
from SigProfilerMatrixGenerator.scripts import SVMatrixGenerator as sv

input_dir = "./SigProfilerMatrixGenerator/references/SV/example_input/560-Breast"
output_dir = "./SigProfilerMatrixGenerator/references/SV/"
project = "560-Breast"

sv.generateSVMatrix(input_dir, project, output_dir)

Command Line Usage

python3 ./SigProfilerMatrixGenerator/scripts/SVMatrixGenerator.py \
    ./SigProfilerMatrixGenerator/references/SV/example_input/560-Breast \
    560-Breast \
    ./SigProfilerMatrixGenerator/references/SV/example_output/

Arguments: input_dir, project, output_dir

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R Usage

Setting up R Environment with Conda (Recommended)

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")
> sv <- SVMatrixGenerator("[your_repo]/test_data/SV", "test_SV", "[your_repo]/test_data/")

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Function Arguments

Required Parameters

Parameter	Type	Description	Example
input_dir	string	Path to directory containing SV bedpe files (one per sample)	"./references/SV/example_input/560-Breast/"
project	string	Project name for this instance	"560-Breast"
output_dir	string	Path to directory for output files	"./references/SV/example_output/"

Note: If the output directory doesn't exist, a new one will be created.

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SV Classification Schema (SV32)

The SV32 classification schema bins structural variants by:

1. Size categories:
2. 0-10kb
3. 10kb-100kb
4. 100kb-1Mb
5. 1Mb-10Mb

6. 10Mb

7. Clustering status:

8. Clustered

9. Non-clustered

10. SV type:

11. Deletion
12. Tandem duplication
13. Inversion
14. Translocation (size-independent)

This creates a 32-channel classification matrix for comprehensive structural variant analysis.