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Extracts mutational signatures from mutational catalogues

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SigProfilerExtractor

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. Detailed documentation can be found at: https://osf.io/t6j7u/wiki/home/

Table of contents

Installation

In the command line, please run the following:

$ 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.

Next, open a python interpreter and import the SigProfilerExtractor module. Please see the examples of the functions.

Functions

The list of available functions are:

  • importdata
  • sigProfilerExtractor
  • estimate_solution
  • decompose

And an additional script:

  • plotActivity.py

importdata

Imports the path of example data.

importdata(datatype="matrix")

importdata 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 use the following:
help(sig.importdata)

sigProfilerExtractor

Extracts mutational signatures from an array of samples.

sigProfilerExtractor(input_type, out_put, input_data, reference_genome="GRCh37", opportunity_genome = "GRCh37", context_type = "default", exome = False, 
                         minimum_signatures=1, maximum_signatures=10, nmf_replicates=100, resample = True, batch_size=1, cpu=-1, gpu=False, 
                         nmf_init="random", precision= "single", matrix_normalization= "gmm", seeds= "none", 
                         min_nmf_iterations= 10000, max_nmf_iterations=1000000, nmf_test_conv= 10000, nmf_tolerance= 1e-15,nnls_add_penalty=0.05,
                         nnls_remove_penalty=0.01, initial_remove_penalty=0.05, de_novo_fit_penalty=0.02,get_all_signature_matrices= False)
Category Parameter Variable Type Parameter Description
Input Data
input_type String The type of input:
  • "vcf": used for vcf format inputs.
  • "matrix": used for table format inputs using a tab seperated file.
out_put String The name of the output folder. The output folder will be generated in the current working directory.
input_data String Name of the input folder (in case of "vcf" type input) or the input file (in case of "table" 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 "text" type projects have to be a file.
reference_genome String The name of the reference genome. The default reference genome is "GRCh37". This parameter is applicable only if the input_type is "vcf".
opportunity_genome String The build or version of the reference signatures for the reference genome. The default opportunity genome is GRCh37. If the input_type is "vcf", the genome_build automatically matches the input reference genome value.
context_type String A string of mutaion context name/names separated by comma (","). The items in the list defines the mutational contexts to be considered to extract the signatures. The default value is "96,DINUC,ID", where "96" is the SBS96 context, "DINUC" is the DINUCLEOTIDE context and ID is INDEL context.
exome Boolean Defines if the exomes will be extracted. The default value is "False".
NMF Replicates
minimum_signatures Positive Integer The minimum number of signatures to be extracted. The default value is 1.
maximum_signatures Positive Integer The maximum number of signatures to be extracted. The default value is 25.
nmf_replicates Positive Integer The number of iteration to be performed to extract each number signature. The default value is 100.
resample Boolean Default is True. If True, add poisson noise to samples by resampling.
seeds String It can be used to get reproducible resamples for the NMF replicates. A path of a tab separated .txt file containing the replicated id and preset seeds in a two columns dataframe can be passed through this parameter. The Seeds.txt file in the results folder from a previous analysis can be used for the seeds parameter in a new analysis. The Default value for this parameter is "none". When "none", the seeds for resampling will be random for different analysis.
NMF Engines
matrix_normalization String Method of normalizing the genome matrix before it is analyzed by NMF. Default is value is "gmm". Other options are, "log2", "custom" or "none".
nmf_init String The initialization algorithm for W and H matrix of NMF. Options are 'random', 'nndsvd', 'nndsvda', 'nndsvdar' and 'nndsvd_min'. Default is 'nndsvd_min'.
precision String Values should be single or double. Default is single.
min_nmf_iterations Integer Value defines the minimum number of iterations to be completed before NMF converges. Default is 10000.
max_nmf_iterations Integer Value defines the maximum number of iterations to be completed before NMF converges. Default is 1000000.
nmf_test_conv Integer Value defines the number number of iterations to done between checking next convergence. Default is 10000.
nmf_tolerance Float Value defines the tolerance to achieve to converge. Default is 1e-15.
Execution
cpu Integer The number of processors to be used to extract the signatures. The default value is -1 which will use all available processors.
gpu Boolean Defines if the GPU resource will used if available. Default is False. If True, the GPU resources will be used in the computation.
batch_size Integer Will be effective only if the GPU is used. Defines the number of NMF replicates to be performed by each CPU during the parallel processing. Default is 1.
Solution Estimation Thresholds
stability Float Default is 0.8. The cutoff thresh-hold of the average stability. Solutions with average stabilities below this thresh-hold will not be considered.
min_stability Float Default is 0.2. The cutoff thresh-hold of the minimum stability. Solutions with minimum stabilities below this thresh-hold will not be considered.
combined_stability Float Default is 1.0. The cutoff thresh-hold of the combined stability (sum of average and minimum stability). Solutions with combined stabilities below this thresh-hold will not be considered.
Decomposition
cosmic_version Float Takes a positive float among 1, 2, 3, 3.1, 3.2. Default is 3.1. Defines the version of COSMIC reference signatures.
de_novo_fit_penalty Float Takes any positive float. Default is 0.02. Defines the weak (remove) thresh-hold cutoff to assign denovo signatures to a sample.
nnls_add_penalty Float Takes any positive float. Default is 0.05. Defines the strong (add) thresh-hold cutoff to assign COSMIC signatures to a sample.
nnls_remove_penalty Float Takes any positive float. Default is 0.01. Defines the weak (remove) thresh-hold cutoff to assign COSMIC signatures to a sample.
initial_remove_penalty Float Takes any positive float. Default is 0.05. Defines the initial weak (remove) thresh-hold cutoff to COSMIC assign signatures to a sample.
refit_denovo_signatures Boolean Default is True. If True, then refit the denovo signatures with nnls.
make_decomposition_plots Boolean Defualt is True. If True, Denovo to Cosmic sigantures decompostion plots will be created as a part the results.
collapse_to_SBS96 Boolean Defualt is True. If True, SBS288 and SBS1536 Denovo signatures will be mapped to SBS96 reference signatures. If False, those will be mapped to reference signatures of the same context.
Others
get_all_signature_matrices Boolean If True, the Ws and Hs from all the NMF iterations are generated in the output.
export_probabilities Boolean Defualt is True. If False, then doesn't create the probability matrix.

sigProfilerExtractor Example

from SigProfilerExtractor import sigpro as sig
def main_function():
    # 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, minimum_signatures=1, maximum_signatures=3)
if __name__="__main__":
   main_function()

#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.

def main_function():    
   # 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("matrix", "example_output", data, opportunity_genome="GRCh38", minimum_signatures=1,            maximum_signatures=3)
if __name__="__main__":
   main_function()

sigProfilerExtractor Output

To learn about the output, please visit https://osf.io/t6j7u/wiki/home/

Estimation of the Optimum Solution

Estimate the optimum solution (rank) among different number of solutions (ranks).

ebs.estimate_solution(base_csvfile="All_solutions_stat.csv", 
          All_solution="All_Solutions", 
          genomes="Samples.txt", 
          output="results", 
          title="Selection_Plot",
          stability=0.8, 
          min_stability=0.2, 
          combined_stability=1.25)
Parameter Variable Type Parameter Description
base_csvfile String Default is "All_solutions_stat.csv". Path to a csv file that contains the statistics of all solutions.
All_solution String Default is "All_Solutions". Path to a folder that contains the results of all solutions.
genomes String Default is Samples.txt. Path to a tab delimilted file that contains the mutation counts for all genomes given to different mutation types.
output String Default is "results". Path to the output folder.
title String Default is "Selection_Plot". This sets the title of the selection_plot.pdf
stability Float Default is 0.8. The cutoff thresh-hold of the average stability. Solutions with average stabilities below this thresh-hold will not be considered.
min_stability Float Default is 0.2. The cutoff thresh-hold of the minimum stability. Solutions with minimum stabilities below this thresh-hold will not be considered.
combined_stability Float Default is 1.0. The cutoff thresh-hold of the combined stability (sum of average and minimum stability). Solutions with combined stabilities below this thresh-hold will not be considered.

Estimation of the Optimum Solution Example

from SigProfilerExtractor import estimate_best_solution as ebs
ebs.estimate_solution(base_csvfile="All_solutions_stat.csv", 
          All_solution="All_Solutions", 
          genomes="Samples.txt", 
          output="results", 
          title="Selection_Plot",
          stability=0.8, 
          min_stability=0.2, 
          combined_stability=1.25)

Estimation of the Optimum Solution Output

The files below will be generated in the output folder: | File Name | Description | | ----- | ----- | | All_solutions_stat.csv | A csv file that contains the statistics of all solutions. | | selection_plot.pdf | A plot that depict the Stability and Mean Sample Cosine Distance for different solutions. |

Decompose

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

decompose(signatures, activities, samples,  output, signature_database=None, nnls_add_penalty=0.05, nnls_remove_penalty=0.01, initial_remove_penalty=0.05, de_novo_fit_penalty=0.02, genome_build="GRCh37", refit_denovo_signatures=True, make_decomposition_plots=True, connected_sigs=True, verbose=False)
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.
de_novo_fit_penalty Float Takes any positive float. Default is 0.02. Defines the weak (remove) thresh-hold cutoff to be assigned denovo signatures to a sample. Optional parameter.
nnls_add_penalty Float Takes any positive float. Default is 0.01. Defines the weak (remove) thresh-hold cutoff to be assigned COSMIC signatures to a sample. Optional parameter.
nnls_remove_penalty Float Takes any positive float. Default is 0.01. Defines the weak (remove) thresh-hold cutoff to be assigned COSMIC signatures to a sample. Optional parameter.
initial_remove_penalty Float Takes any positive float. Default is 0.05. Defines the initial weak (remove) thresh-hold cutoff to be COSMIC assigned signatures to a sample. Optional parameter.
genome_build String The genome type. Example: "GRCh37", "GRCh38", "mm9", "mm10". The default value is "GRCh37"
verbose Boolean Prints statements. Default value is False.

Decompose Example

from SigProfilerExtractor import decomposition as decomp
signatures = "path/to/De_Novo_Solution_Signatures.txt"
activities="path/to/De_Novo_Solution_Activities.txt"
samples="path/to/Samples.txt"
output="name or path/to/output"
decomp.decompose(signatures, activities, samples, output, genome_build="GRCh37", verbose=False)

Decompose Output

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

Activity Stacked Bar Plot

Generates a stacked bar plot showing activities in individuals

plotActivity(activity_file, output_file = "Activity_in_samples.pdf", bin_size = 50, log = False)
Parameter Variable Type Parameter Description
activity_file String The standard output activity file showing the number of, or percentage of mutations attributed to each sample. The row names should be samples while the column names should be signatures.
output_file String The path and full name of the output pdf file, including ".pdf"
bin_size Integer Number of samples plotted per page, recommended: 50

Activity Stacked Bar Plot Example

$ python plotActivity.py 50 sig_attribution_sample.txt test_out.pdf

Video Tutorials

Take a look at our video tutorials for step-by-step instructions on how to install and run SigProfilerExtractor on Amazon Web Services.

Tutorial #1: Installing SigProfilerExtractor on Amazon Web Services

Video Tutorial #3

Tutorial #2: Running the Quick Start Example Program

Video Tutorial #3

Tutorial #3: Reviewing the output from SigProfilerExtractor

Video Tutorial #3

GPU support

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/

Copyright

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.

Contact Information

Please address any queries or bug reports to S M Ashiqul Islam (Mishu) at m0islam@ucsd.edu

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