A framework to infer mutational signatures over time.
Cell processes leave a unique signature of mutation types in cancer genome. Using the mutational signatures, it is possible to infer the fraction of mutations contributed by each cell process. Mutational signatures are represented as multinomial distributions over 96 mutation types. Using our framework Trackature, we can infer mutational signatures changing over time.
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Python 2.7.9
Packages: pyvcf, csv, scipy, numpy
Packages can be installed usingpip2 install package_namecommand. -
Perl v5.18.2
Packages: Bio::DB::Fasta. Please refer to http://www.cpan.org/modules/INSTALL.html on how to install packages on your machine. On Mac OS and Unix: sudo cpan Bio::DB::Fasta -
R 3.1.2
Packages: reshape2, ggplot2, NMF
R packages can be installed using install.packages("package_name") command.
- VCF file with point mutations
VCF file should be named as tumor_id.vcf, where tumor_id is an id of the tumor.
INFO column in VCF file should contain reference and alternate read counts in the following format: "t_alt_count=5;t_ref_count=20".
Optional:
- Sample purity
The format is the following: (tab-delimited)
samplename purity
example 0.7
The file should contain for purities for all your samples. The "samplename" column should match the name of the vcf file. Please refer to the example in data/example_purity.txt
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Copy number alteration calls
The format is the following: (tab-delimited)
chromosome start end total_cn
1 2888343 3263790 3
The commands below assume starting from the 'Trackature' directory. The example.vcf is provided in the repo. Running the code as written below will compute signature trajectories for the example.
We use variant allele frequency (VAF) to sort mutations by the order of their occurrence.
To generate VAF values:
python src/make_corrected_vaf.py --vcf data/example.vcf --output data/example_vaf.txt
It is recommended to correct VAF by copy number and tumor purity if those are available. You can specify file CNA calls and a file containing sample purities the following way:
python src/make_corrected_vaf.py --vcf data/example.vcf --cnv your_cna_call_file.txt --purity purity_file.txt --output data/example_vaf.txt
Please refer to the example for the format of tumor purity file. The file should be tab-delimited in the following format:
samplename purity
example 0.7
The "samplename" column should match the name of the vcf file. To make use of purities when correcting VAF, provide the name of the purity file to make_corrected_vaf.py with parameter "--purity".
To make mutation counts over 96 mutation types:
src/make_counts.sh data/example.vcf data/example_vaf.txt
where the first parameter is the vcf file and the second parameter is file with VAF values generated at the previous step.
Requires hg19 reference which can be downloaded from here: http://hgdownload.cse.ucsc.edu/goldenPath/hg19/chromosomes/
Using an rsync command to download all the hg19 reference files:
rsync -avzP rsync://hgdownload.cse.ucsc.edu/goldenPath/hg19/chromosomes/ ./annotation/hg19/
To use cancer-type specific signatures, please provide data/tumortypes.txt file listing tumor IDs and their cancer types in the format (tab-delimited):
ID tumortype
example LAML
The names of the cancer types must match the ones in annotation/active_signatures_transposed.txt table which lists active signatures for TCGA cancer types.
Modify src/header.R to set up the path to your data
tumortype_file <- "data/tumortypes.txt"
Compute signature trajectories for all samples:
Rscript src/compute_mutational_signatures.R
Results can be found in "results_signature_trajectories" folder (by default, specified in by DIR_RESULTS in src/header.R) in appropriate cancer type and tumor id folders. Signature trajectories are stored in mixtures.csv. Rows correspond to signatures. Columns correspond to time points. The columns are named by the average cellular prevalence that corresponds to the time point.
If you wish to compute uncertainty for trajectories as well, set compute_bootstrap parameter in src/header.R to TRUE before running the script (slows down the computation).
python src/generate_simulations.py
Output: files with simulated mutation counts and true signature activities in simulated_data/ folder
Optional arguments:
--timepoints -- number of time points (50 by default)
--sig-file -- file with mutational siggnatures ("annotation/alexSignatures_w_header.csv" by default)
The simulations use a different format of mutation counts. If you want to run TrackSig on simulations, be sure to set simulated_data = TRUE in src/header.R.
Finally, run TrackSig on simulations:
Rscript src/compute_mutational_signatures.R
Mean signature activities across all mutations in the tumor can be computed the following way:
R
source("src/header.R")
compute_overall_exposures_for_all_examples()
It will compute the signature activities (aka "mixtures") for all samples in the data/counts directory. The results can be found in the results directory under the appropriate cancer type and tumor ID ( overall_mixtures.csv).
Alternatively, you can call a function to compute activities directly:
mixtures <- fit_mixture_of_multinomials_EM(mutation_counts, alex.t)
Input to fit_mixture_of_multinomials_EM:
- mutation_counts : 1x96 data frame specifying mutation counts over 96 types
- alex.t : specifies signatures to fit (96 by number of signatures).
Using COSMIC per-cancer active signatures
Active signatures vary across cancer types. To ensure that we fit only most relevant signatures for the particular cancer type, we refer to the table of active signatures from COSMIC, reproduced in annotation/active_signatures_transposed.txt file.
To use cancer-type specific signatures, please provide data/tumortypes.txt file listing tumor IDs and their cancer types (see example in the repo). The names of the cancer types must match the onces in active_signatures_transposed.txt table.
Estimating active signatures from scratch
If active signatures are unavailable, they can be estimated by computing mean activities across all mutations (see "Computing overall signature activities" section) and taking the signatures with highest activities (for example, with activity > 10%). Next, these signatures should be specified as active in src/header.R before running src/compute_mutational_signatures.R.
We recommmend to estimate active signatures first (as described above) instead of fitting all signatures over time through Trackature, as it provides more stable results and speeds up the computation.
To specify a separate set of active signatures for each sample:
- in
src/header.Rset cancer_type_signatures = FALSE - in
src/header.Rset active_signatures_file to the file with active signatures per sample. See example of such file inannotation/active_in_samples.txt.
Signatures provided in the repo are from COSMIC located in annotation/alexSignatures.txt. You can use your own signatures by providing path to another signature file through signature_file parameter in src/header.R.
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Is not applicable for samples with <600 mutations. Please note that Trackature does not run on samples with less than 600 mutations. Less than 600 mutations will result in less than 3 time points, and there is no point to analize it as a time series. On tumors with less than 600 mutations, you can compute signature activities without dividing mutations into time points (see "Computing overall signature activities" section).
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Results are not re-computed when script is re-started. Please note that at every step if you stop the script and re-start it, the computations will continue instead of re-writing the previous results. It is useful for launching large batches of samples: scripts can be paused when needed; if one sample fails, other samples don't need to be re-computed again. However, if you wish some results to be re-computed, please erase the corresponding directory.
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If the tumour names contain a dot, please replace it with another symbol, for example, with underscore.