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/
To install the current version of this Github repo, git clone this repo or download the zip file. Unzip the contents of SigProfilerExtractor-master.zip or the zip file of a corresponding branch.
In the command line, please run the following:
$ cd SigProfilerExtractor-master
$ pip install .For most recent stable pypi version of this tool, In the command line, please run the following:
$ pip install SigProfilerExtractorInstall 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.
The list of available functions are:
- importdata
- sigProfilerExtractor
- estimate_solution
- decompose
And an additional script:
- plotActivity.py
Imports the path of example data.
importdata(datatype="matrix")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)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= "random",
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:
|
|
| 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 "random". When "random", 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 'random'. | |
| 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. Note: All available CPU processors are used by default, which may cause a memory error. This error can be resolved by reducing the number of CPU processes through the cpu parameter. | |
| 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. |
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()To learn about the output, please visit https://osf.io/t6j7u/wiki/home/
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. |
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)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. |
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,collapse_to_SBS96=True,newsignature_threshold=0.8)| 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. |
| signature_database | String or None | Path to custom database. Default is None |
| 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. |
| collapse_to_SBS96 | Boolean | It collapses the signatures of context type 288 and 1536 to 96. Use False if your custom signature database is 288 or 1536 and signatures are also of the same context type other than 96. Default value is True. |
| newsignature_threshold | Float | Threshold on cosine similarity to assign a new signature. Default value is 0.8. |
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", signature_database=None, verbose=False,collapse_to_SBS96=True)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
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 |
$ python plotActivity.py 50 sig_attribution_sample.txt test_out.pdfTake a look at our video tutorials for step-by-step instructions on how to install and run SigProfilerExtractor on Amazon Web Services.
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 Mark Barnes at mdbarnes@ucsd.edu