SigProfilerAssignment 0.0.18

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 Subgroups

exclude_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 excluded	DBS signatures excluded	ID signatures excluded
MMR_deficiency_signatures	6, 14, 15, 20, 21, 26, 44	7, 10	7
POL_deficiency_signatures	10a, 10b, 10c, 10d, 28	3	-
HR_deficiency_signatures	3	-	6
BER_deficiency_signatures	30, 36	-	-
Chemotherapy_signatures	11, 25, 31, 35, 86, 87, 90	5	-
Immunosuppressants_signatures	32	-	-
Treatment_signatures	11, 25, 31, 32, 35, 86, 87, 90	5	-
APOBEC_signatures	2, 13	-	-
Tobacco_signatures	4, 29, 92	2	3
UV_signatures	7a, 7b, 7c, 7d, 38	1	13
AA_signatures	22	-	-
Colibactin_signatures	88	-	18
Artifact_signatures	27, 43, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 95	-	-
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,
                       exclude_signature_subgroups=exclude_signature_subgroups,
                       exome=False)

Analysis

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,
                    make_plots=True,
                    exclude_signature_subgroups=exclude_signature_subgroups,
                    exome=False
)

Main Parameters

Parameter	Variable Type	Parameter Description
samples	String	Path to input file for input_type: "matrix" "seg:TYPE" Path to input folder for input_type: "vcf"
output	String	Path to the output folder.
input_type	String	The type of input: "matrix": used for table format inputs using a tab-separated file where the rows are mutation types and the columns are sample IDs. "vcf": used for mutation calling file inputs (VCFs, MAFs or simple text files). "seg:TYPE": used for a multi-sample segmentation file for copy number analysis. The accepted callers for TYPE are the following {"ASCAT", "ASCAT_NGS", "SEQUENZA", "ABSOLUTE", "BATTENBERG", "FACETS", "PURPLE", "TCGA"}. For example, when using segmentation file from BATTENBERG then set input_type to "seg:BATTENBERG". The default value is "matrix".
context_type	String	Required context type if input_type is "vcf". context_type takes which context type of the input data is considered for assignment. Valid options include "96", "288", "1536", "DINUC", and "INDEL". The default value is "96".
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.
exclude_signature_subgroups	List	Removes the signatures corresponding to specific subtypes for better fitting. The usage is given above. The default value is None.
exome	Boolean	Defines if the exome renormalized signatures will be used. The default value is False.
export_probabilities	Boolean	Defines if the probability matrix is created. The default value is True.
make_plots	Boolean	Toggle on and off for making and saving all plots. The default value is True.
verbose	Boolean	Prints statements. The default value is False.

Examples

SPA analysis - Example for a matrix

#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,
                    exclude_signature_subgroups=None,
                    exome=False)

SPA analysis - Example for input vcf files

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

#set directories and paths to signatures and samples
dir_inp = os.path.join(spa.__path__[0], '/data/Examples/')
# directory of vcf files
samples = os.path.join(spa.__path__[0], '/data/tests/vcf_input/')
output = "output_example/"
signatures = os.path.join(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,
                    input_type="vcf",
                    context_type="96", 
                    signatures=None,
                    signature_database=sigs,
                    genome_build="GRCh37",
                    cosmic_version=3.3,
                    verbose=False,
                    collapse_to_SBS96=False,
                    make_plots=True,
                    exclude_signature_subgroups=None,
                    exome=False)

SPA analysis - Example for an input multi-sample segmentation file

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

#set directories and paths to signatures and samples
dir_inp = os.path.join(spa.__path__[0], 'data/Examples/')
# segmentation file
samples = os.path.join(spa.__path__[0], \
    '/data/tests/cnv_input/all.breast.ascat.summary.sample.tsv')
output = "output_example/"

#Analysis of SP Assignment 
Analyze.cosmic_fit( samples, 
                    output,
                    input_type="seg:ASCAT_NGS",
                    context_type="CNV48", 
                    signatures=None,
                    signature_database=None,
                    genome_build="GRCh37",
                    cosmic_version=3.3,
                    verbose=False,
                    collapse_to_SBS96=False,
                    make_plots=True,
                    exclude_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 [e-mail]