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Mutational signatures attribution and decomposition tool

Project description

License Build Status

drawing

SigProfilerAssignment

SigProfilerAssignment is a new mutational attribution and decomposition tool that performs the following functions:

  • Attributing a known set of mutational signatures to an individual sample or multiple samples.
  • Decomposing de novo signatures to COSMIC signature database.
  • Attributing COSMIC database or a custom signature database to given samples.

The tool identifies the activity of each signature in the sample and assigns the probability for each signature to cause a specific mutation type in the sample. The tool makes use of SigProfilerMatrixGenerator, SigProfilerExtractor and SigProfilerPlotting.

Installs

for installing from PyPi in new conda environment

$ pip install SigProfilerAssignment

Installing this package : git clone this repo or download the zip file. Unzip the contents of SigProfilerExtractor-master.zip or the zip file of a corresponding branch.

$ cd SigProfilerAssignment-master
$ pip install .

Signature Subtypes

signature_subgroups = ['remove_MMR_deficiency_signatures',
                       'remove_POL_deficiency_signatures',
                       'remove_HR_deficiency_signatures' ,
                       'remove_BER_deficiency_signatures',
                       'remove_Chemotherapy_signatures',
                       'remove_Immunosuppressants_signatures'
                       'remove_Treatment_signatures'
                       'remove_APOBEC_signatures',
                       'remove_Tobacco_signatures',
                       'remove_UV_signatures',
                       'remove_AA_signatures',
                       'remove_Colibactin_signatures',
                       'remove_Artifact_signatures',
                       'remove_Lymphoid_signatures']
Signature Subgroup SBS Signatures that are excluded
MMR_deficiency_signatures 6, 14, 15, 20, 21, 26, 44
POL_deficiency_signatures 10a, 10b, 10c, 10d, 28
HR_deficiency_signatures 3
BER_deficiency_signatures 30, 36
Chemotherapy_signatures 11, 25, 31, 35, 86, 87, 90
Immunosuppressants_signatures 32
Treatment_signatures 11, 25, 31, 32, 35, 86, 87, 90
APOBEC_signatures 2, 13
Tobacco_signatures 4, 29, 92
UV_signatures 7a, 7b, 7c, 7d, 38
AA_signatures 22
Colibactin_signatures 88
Artifact_signatures 27, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60
Lymphoid_signatures 9, 84, 85

Decompose Fit

Decomposes the De Novo Signatures into COSMIC Signatures and assigns COSMIC signatures into samples. drawing

from SigProfilerAssignment import Analyzer as Analyze
Analyze.decompose_fit(samples, 
                       output, 
                       signatures=signatures,
                       signature_database=sigs,
                       genome_build="GRCh37", 
                       verbose=False,
                       new_signature_thresh_hold=0.8,
                       signature_subgroups=signature_subgroups,
                       exome=False)

De Novo Fit

Attributes mutations of given Samples to input denovo signatures. drawing

from SigProfilerAssignment import Analyzer as Analyze
Analyze.denovo_fit( samples,
                    output, 
                    signatures=signatures,
                    signature_database=sigs,
                    genome_build="GRCh37", 
                    verbose=False)

COSMIC Fit

Attributes mutations of given Samples to input COSMIC signatures. Note that penalties associated with denovo fit and COSMIC fits are different. drawing

from SigProfilerAssignment import Analyzer as Analyze
Analyze.cosmic_fit( samples, 
                    output, 
                    signatures=None,
                    signature_database=sigs,
                    genome_build="GRCh37", 
                    verbose=False,
                    collapse_to_SBS96=False,
                    make_plots=True,
                    signature_subgroups=signature_subgroups,
                    exome=False
)

Main Parameters

Parameter Variable Type Parameter Description
samples String Path to a tab delimilted file that contains the samples table where the rows are mutation types and colunms are sample IDs.
output String Path to the output folder.
signatures String Path to a tab delimited file that contains the signature table where the rows are mutation types and colunms are signature IDs.
genome_build String The reference genome build. List of supported genomes: "GRCh37", "GRCh38", "mm9", "mm10" and "rn6". The default value is "GRCh37". If the selected genome is not in the supported list, the default genome will be used.
cosmic_version Float Takes a positive float among 1, 2, 3, 3.1, 3.2 and 3.3. Defines the version of the COSMIC reference signatures. The default value is 3.3.
new_signature_thresh_hold Float Parameter in cosine similarity to declare a new signature. Applicable for decompose_fit only. The default value is 0.8.
make_plots Boolean Toggle on and off for making and saving all plots. Default value is True.
signature_subgroups List Removes the signatures corresponding to specific subtypes for better fitting. The usage is given above. Default value is None.
exome Boolean Defines if the exome renormalized signatures will be used. The default value is False.
verbose Boolean Prints statements. Default value is False.

SPA analysis Example

#import modules
import SigProfilerAssignment as spa
from SigProfilerAssignment import Analyzer as Analyze

#set directories and paths to signatures and samples
dir_inp     = spa.__path__[0]+'/data/Examples/'
samples     = dir_inp+"Input_scenario_8/Samples.txt"
output      = "output_example/"
signatures  = dir_inp+"Results_scenario_8/SBS96/All_Solutions/SBS96_3_Signatures/Signatures/SBS96_S3_Signatures.txt"
sigs        = "COSMIC_v3_SBS_GRCh37_noSBS84-85.txt" #Custom Signature Database

#Analysis of SP Assignment 
Analyze.cosmic_fit( samples, 
                    output, 
                    signatures=None,
                    signature_database=sigs,
                    genome_build="GRCh37",
                    cosmic_version=3.3,
                    verbose=False,
                    collapse_to_SBS96=False,
                    make_plots=True,
                    signature_subgroups=None,
                    exome=False)

Copyright

This software and its documentation are copyright 2022 as a part of the SigProfiler project. The SigProfilerAssignment framework is free software and is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

Contact Information

Please address any queries or bug reports to Raviteja Vangara at rvangara@health.ucsd.edu

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