SigProfilerExtractor allows de novo extraction of mutational signatures from data generated in a matrix format. The tool identifies the number of operative mutational signatures, their activities in each sample, and the probability for each signature to cause a specific mutation type in a cancer sample. The tool makes use of SigProfilerMatrixGenerator and SigProfilerPlotting.
In the commandline, please type the following line:
$pip install SigProfilerExtractor
Install your desired reference genome from the command line/terminal as follows (available reference genomes are: GRCh37, GRCh38, mm9, and mm10):
$ python
>> from SigProfilerMatrixGenerator import install as genInstall
>> genInstall.install('GRCh37')
This will install the human 37 assembly as a reference genome. You may install as many genomes as you wish.
open a python interpreter and import the SigProfilerExtractor module. Please see the examples of the functions.
Imports the path of example data.
importdata(datatype="matrix")
Example:
-------
>>> from SigProfilerExtractor import sigpro as sig
>>> path_to_example_table = sig.importdata("matrix")
>>> data = path_to_example_table
This "data" variable can be used as a parameter of the "project" argument of the sigProfilerExtractor function.
To get help on the parameters and outputs of the "importdata" function, please write down the following line:
>>> help(sig.importdata)
Extracts mutational signatures from an array of samples.
sigProfilerExtractor(input_type, out_put, input_data, refgen="GRCh37", genome_build = 'GRCh37', startProcess=1, endProcess=10, totalIterations=100, init="alexandrov-lab-custom", cpu=-1, mtype = "default",exome = False, penalty=0.05, resample = True, wall= False, gpu=False)
input_type: A string. Type of input. The type of input should be one of the following: - "vcf": used for vcf format inputs. - "matrix": used for table format inputs using a tab seperated file.
out_put: A string. The name of the output folder. The output folder will be generated in the current working directory.
input_data: A string. Name of the input folder (in case of "vcf" type input) or the input file (in case of "matrix" type input). The project file or folder should be inside the current working directory. For the "vcf" type input,the project has to be a folder which will contain the vcf files in vcf format or text formats.The "matrix" type projects have to be a file.
refgen: A string, optional. The name of the reference genome. The default reference genome is "GRCh37". This parameter is applicable only if the input_type is "vcf".
startProcess: A positive integer, optional. The minimum number of signatures to be extracted. The default value is 1
endProcess: A positive integer, optional. The maximum number of signatures to be extracted. The default value is 10
totalIterations: A positive integer, optional. The number of iteration to be performed to extract each number signature. The default value is 100
init: A String. The initialization algorithm for W and H matrix of NMF. Options are "alexandrov-lab-custom", "nndsvd", nndsvda", "nndsvdar".
wall: A Boolean. If true, the Ws and Hs from all the NMF iterations are generated in the output.
cpu: An integer, optional. The number of processors to be used to extract the signatures. The default value is -1 which will use all available processors.
mtype: A string, optional. The items in the list defines the mutational contexts to be considered to extract the signatures. The default value is "96,DBS,ID", where "96" is the SBS96 context, "DBS" is the DINULEOTIDE context and ID is INDEL context.
exome: Boolean, optional. Defines if the exomes will be extracted. The default value is "False".
penalty: Float, optional. Takes any positive float. Default is 0.05. Defines the thresh-hold cutoff to asaign signatures to a sample.
resample: Boolean, optional. Default is True. If True, add poisson noise to samples by resampling.
To learn about the output, please visit https://osf.io/t6j7u/wiki/home/
Examples
--------
>>> from SigProfilerExtractor import sigpro as sig
# to get input from vcf files
>>> path_to_example_folder_containing_vcf_files = sig.importdata("vcf")
>>> data = path_to_example_folder_containing_vcf_files # you can put the path to your folder containing the vcf samples
>>> sig.sigProfilerExtractor("vcf", "example_output", data, startProcess=1, endProcess=3)
Wait untill the excecution is finished. The process may a couple of hours based on the size of the data.
Check the current working directory for the "example_output" folder.
# to get input from table format (mutation catalog matrix)
>>> path_to_example_table = sig.importdata("matrix")
>>> data = path_to_example_table # you can put the path to your tab delimited file containing the mutational catalog matrix/table
>>> sig.sigProfilerExtractor("table", "example_output", data, genome_build="GRCh38", startProcess=1, endProcess=3)
To get help on the parameters and outputs of the "sigProfilerExtractor" function, please write down the following line:
>>> help(sig.sigProfilerExtractor)
Decomposes the De Novo Signatures into COSMIC Signatures and assigns COSMIC signatures into samples
Parameters:
signatures: A string. Path to a tab delimited file that contains the signaure table where the rows are mutation types and colunms are signature IDs.
activities: A string. Path to a tab delimilted file that contains the activity table where the rows are sample IDs and colunms are signature IDs.
samples: A string. Path to a tab delimilted file that contains the activity table where the rows are mutation types and colunms are sample IDs.
output: A string. Path to the output folder.
genome_build = A string. The genome type. Example: "GRCh37", "GRCh38", "mm9", "mm10". The default value is "GRCh37"
verbose = Boolean. Prints statements. Default value is False.
Example:
>>>from SigProfilerExtractor import decomposition as decomp
>>>signatures = "path/to/dDe_Novo_Solution_Signatures.txt"
>>>activities="path/to/De_Novo_Solution_Activities.txt"
>>>samples="path/to/Samples.txt"
>>>output="name or path/to/output.txt"
>>>decomp.decompose(signatures, activities, samples, output, genome_build="GRCh37", verbose=False)
Values:
The files below will be generated in the output folder--
Cluster_of_Samples.txt
comparison_with_global_ID_signatures.csv
Decomposed_Solution_Activities.txt
Decomposed_Solution_Samples_stats.txt
Decomposed_Solution_Signatures.txt
decomposition_logfile.txt
dendogram.pdf
Mutation_Probabilities.txt
Signature_assaignment_logfile.txt
Signature_plot[MutatutionContext]_plots_Decomposed_Solution.pdf
Sigprofilerextractor is GPU-enabled and can run on single or multi-GPU systems for significantly increased performance in most circumstances.
To use this feature set the GPU flag to True:
sigProfilerExtractor(input_type, out_put, project, refgen="GRCh37", genome_build = "GRCh37", startProcess=1, endProcess=10, totalIterations=100,
cpu=-1, exome = False, gpu=True)
If CUDA out of memory exceptions occur, it will be necessary to reduce the number of CPU processes used (the cpu parameter).
For more information, help and examples, please visit: https://osf.io/t6j7u/wiki/home/
This software and its documentation are copyright 2018 as a part of the sigProfiler project. The SigProfilerExtractor 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.
Please address any queries or bug reports to S M Ashiqul Islam (Mishu) at m0islam.ucsd.edu