SigProfilerAssignment 0.0.9

Mutational signatures attribution and decomposition tool

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.

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)

De Novo Fit

Attributes mutations of given Samples to input denovo signatures.

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.

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,
                    signature_subgroups=signature_subgroups,
                    make_plots=True)

Main Parameters

Parameter	Variable Type	Parameter Description
signatures	String	Path to a tab delimited file that contains the signaure table where the rows are mutation types and colunms are signature IDs.
activities	String	Path to a tab delimilted file that contains the activity table where the rows are sample IDs and colunms are signature IDs.
samples	String	Path to a tab delimilted file that contains the activity table where the rows are mutation types and colunms are sample IDs.
output	String	Path to the output folder.
genome_build	String	The genome type. Example: "GRCh37", "GRCh38", "mm9", "mm10". The default value is "GRCh37"
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.
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/'
signatures  = dir_inp+"Results_scenario_8/SBS96/All_Solutions/SBS96_3_Signatures/Signatures/SBS96_S3_Signatures.txt"
activities  = dir_inp+"Results_scenario_8/SBS96/All_Solutions/SBS96_3_Signatures/Activities/SBS96_S3_NMF_Activities.txt"
samples     = dir_inp+"Input_scenario_8/Samples.txt"
output      = "output_example/"
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", 
                    verbose=False,
                    collapse_to_SBS96=False,
                    signature_subgroups=signature_subgroups,
                    make_plots=True)

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 [e-mail]