Tool for analyzing the inter-mutational distances between SNV-SNV and INDEL-INDEL mutations. Tool separates mutations into clustered and non-clustered groups on a sample-dependent basis and subclassifies all SNVs into a set category of clustered event: i) DBS; ii) MBS; iii) omikli; and iv) kataegis. Indels are not subclassifed.
This tool was previously under the project name of SigProfilerHotSpots, but has been renamed to SigProfilerClusters. For all instructions below, SigProfilerClusters may be interchanged with SigProfilerHotSpots if the older version of the tools is being used.
INTRODUCTION
The purpose of this document is to provide a guide for using the SigProfilerClusters framework. An extensive Wiki page detailing the usage of this tool can be found at https://osf.io/qpmzw/wiki/home/.
PREREQUISITES
The framework is written in PYTHON, and uses additional SigProfiler packages:
- PYTHON version 3.8 or newer
- SigProfilerMatrixGenerator (https://github.com/AlexandrovLab/SigProfilerMatrixGenerator)
- SigProfilerSimulator (https://github.com/AlexandrovLab/SigProfilerSimulator)
Please visit their respective GitHub pages for detailed installation and usage instructions.
QUICK START GUIDE
This section will guide you through the minimum steps required to perform clustered analysis:
1a. Install the python package using pip (current package): pip install SigProfilerClusters
1b. Install the python package using pip (deprecated version): pip install SigProfilerHotSpots
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', rsync=False, bash=True)
This will install the human 37 assembly as a reference genome. You may install as many genomes as you wish. If you have a firewall on your server, you may need to install rsync and use the rsync=True parameter. Similarly, if you do not have bash, use bash=False.
- Place your vcf files in your desired output folder. It is recommended that you name this folder based on your project's name. Before you can analyze clustered mutations, you need to generate a background model for each of your samples. To do this, generate a minimum of 100 simulations for your project (see SigProfilerSimulator for a detailed list of parameters):
>>from SigProfilerSimulator import SigProfilerSimulator as sigSim
>>sigSim.SigProfilerSimulator(project, project_path, genome, contexts=["96"], simulations=100, chrom_based=True)
- Now the original mutations can be partitioned into clustered and non-clustered sets using the required parameters below:
>> from SigProfilerClusters import SigProfilerClusters as hp
>> hp.analysis(project, genome, contexts, simContext, input_path)
See below for a detailed list of available parameters
- The partitioned vcf files are placed under [project_path]/ouput/clustered/ and [project_path]/ouput/nonClustered/. You can visualize the results by looking at the IMD plots available under [project_path]/ouput/plots/.
AVAILABLE PARAMETERS
| Parameter | Variable Type | Description |
|---|---|---|
project |
String | Unique name for the given project. |
genome |
String | Reference genome to use. Must be installed using SigProfilerMatrixGenerator. |
contexts |
String | Contexts needs to be one of the following: "96", "ID". |
simContext |
List of Strings | Mutation context used for generating the background model (e.g., ["6144"] or ["96"]). |
input_path |
String | Path to the input files. Must end with a /, e.g., "path/to/the/input_file/". |
| Parameter | Variable Type | Description |
|---|---|---|
analysis |
String | Desired analysis pipeline. Options include "all" (default), "subClassify", and "hotspot". |
sortSims |
Boolean | Option to sort simulated files. Ensures accuracy. Default: True. |
interdistance |
String | Mutation types to calculate IMDs between. Use only for indel analysis. Default: "ID". |
calculateIMD |
Boolean | Whether to calculate IMDs. Useful for rerunning subclassification only. Default: True. |
max_cpu |
Integer | Number of CPUs to use. Default: all available CPUs. |
subClassify |
Boolean | Subclassify clustered mutations (requires VAF scores in TCGA/Sanger format). Default: False. |
plotIMDfigure |
Boolean | Generate IMD and mutational spectra plots for each sample. Default: True. |
plotRainfall |
Boolean | Generate rainfall plots using subclassified clustered events. Default: True. |
| Parameter | Variable Type | Description |
|---|---|---|
includedVAFs |
Boolean | Indicates VAFs are included in the dataset. Default: True. |
includedCCFs |
Boolean | Indicates CCFs are included. If True, set includedVAFs=False. Default: True. |
variant_caller |
String | Format of VAF scores (e.g., "standard"). Default: "standard". |
windowSize |
Integer | Window size for calculating mutation density in rainfall plots. Default: 10000000. |
correction |
Boolean | Perform genome-wide mutational density correction. Default: False. |
probability |
Boolean | Calculate the probability of observing each clustered event in its local region. Output saved in [project_path]/output/clustered/. Default: False. |
VAF Format
SigProfilerClusters uses the VAF recorded in the input files to subclassify clustered mutations when subclassify=True and includedVAFs=True. However, depending on the VCF file, the VAF may be recorded in a different format. Below are the accepted formats corresponding to the correct settings to use for this tool.
If you are not using VCFs as input files, VAFs cannot be used in the subclassification step. Therefore, to subclassify clusters using other input file types set subclassify=True and includedVAFs=False.
If your VAF is recorded in the 11th column of your VCF as the last number of the colon delimited values, set variant_caller="caveman".
If your VAF is recorded in the 8th or 10th column of your VCF as VAF=xx or AF=xx, set variant_caller="standard".
If your VAF is recorded in the 10th or 11th column of your VCF as AF=xx, set variant_caller="mutect2".
If your VCFs have no recorded VAFs set includedVAFs=False. This will run SigProfilerClusters, subclassify clusters based on just the calculated IMD (provided that you set subclassify=True).
CCF Format Alternatively, SigProfilerClusters accepts cancer cell fraction (CCF) estimates in place of VAFs, when includedCCFs=True and includedVAFs=False to correct for copy number amplifications. To use CCFs, one must add the CCF values in the last column of each row within each VCF file (tab separated columns).
Output Format All mutations are categorized into either clustered or non-clustered mutations. Each are saved under their respective directories within VCF files ([project_path]/output/clustered/ or [project_path]/output/nonClustered/; respectively). The clustered folder contains the partition of clustered mutations, which have been subclassified into one of four categories (five if using VAFs/CCFs). Within each subclass subfolder, a single VCF file is saved for each sample. All visualizations including rainfall plots are found within the output directory ([project_oath]/output/plots/). Please refer to the Output page on the OSF wiki page for more details on all available output including available plots (https://osf.io/qpmzw/wiki/4.%20Output/).
LOG FILES
All errors and progress checkpoints are saved into SigProfilerClusters_[project][genome].err and SigProfilerClusters[project]_[genome].out, respectively. For all errors, please email the error and progress log files to the primary contact under CONTACT INFORMATION.
Example Files Two examples are provided under examples/. These directories include all expected output except for the simulations folder to reduce memory size (please see the OSF page for more details: https://osf.io/qpmzw/wiki/4.%20Output/).
CITATIONS
Erik N Bergstrom, Mousumy Kundu, Noura Tbeileh, Ludmil B Alexandrov. bioRxiv 2022.02.11.480117; doi: https://doi.org/10.1101/2022.02.11.480117
COPYRIGHT
Copyright (c) 2023, Erik Bergstrom [Alexandrov Lab] All rights reserved.
Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
CONTACT INFORMATION
Please address any queries or bug reports to Erik Bergstrom at [email protected] or Mousumy Kundu at [email protected]