Automatic Relevance Determination (ARD) - NMF of mutational signature & expression data. Designed for scalability using Pytorch to run using GPUs if available.
Requires Python 3.6.0 or higher.
Please visit our wiki for full documentation.
pip3 install signatureanalyzer
or
git clone --recursive https://github.com/broadinstitute/getzlab-SignatureAnalyzer.git
cd getzlab-SignatureAnalyzer
pip3 install -e .
Note --recurisve
flag is required to clone submodules.
Link: http://gcr.io/broad-cga-sanand-gtex/signatureanalyzer
docker pull gcr.io/broad-cga-sanand-gtex/signatureanalyzer:latest
docker run -it --rm gcr.io/broad-cga-sanand-gtex/signatureanalyzer
PCAWG Mutational Signatures
- Alexandrov, L. B., Kim, J., Haradhvala, N. J., Huang, M. N., Ng, A. W. T., Wu, Y., ... & Islam, S. A. (2020). The repertoire of mutational signatures in human cancer. Nature, 578(7793), 94-101.
- see: https://www.nature.com/articles/s41586-020-1943-3
- see
./PCAWG/
SignatureAnalyzer-GPU source publication
- Taylor-Weiner, A., Aguet, F., Haradhvala, N.J. et al. Scaling computational genomics to millions of individuals with GPUs. Genome Biol 20, 228 (2019) doi:10.1186/s13059-019-1836-7 (https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1836-7)
SignatureAnalyzer-CPU source publications
-
Kim, J. et al. Somatic ERCC2 mutations are associated with a distinct genomic signature in urothelial tumors. Nat. Genet. 48, 600–606 (2016). (https://www.nature.com/articles/ng.3557)
-
Kasar, S. et al. Whole-genome sequencing reveals activation-induced cytidine deaminase signatures during indolent chronic lymphocytic leukaemia evolution. Nat. Commun. 6, 8866 (2015). (https://www.nature.com/articles/ncomms9866)
Mathematical details
- Tan, V. Y. F., Edric, C. & Evotte, F. Automatic Relevance Determination in Nonnegative Matrix Factorization with the β-Divergence. (2012). (https://arxiv.org/pdf/1111.6085.pdf)
usage: signatureanalyzer [-h] [-t {maf,spectra,matrix}] [-n NRUNS] [-o OUTDIR]
[--reference {cosmic2,cosmic3,cosmic3_exome,cosmic3_DBS,cosmic3_ID,cosmic3_TSB,
pcawg_COMPOSITE, pcawg_COMPOSITE96, pcawg_SBS_ID, pcawg_SBS96_ID, pcawg_SBS,
polymerase_msi, polymerase_msi96}]
[--hg_build HG_BUILD] [--cuda_int CUDA_INT]
[--verbose] [--K0 K0] [--max_iter MAX_ITER]
[--del_ DEL_] [--tolerance TOLERANCE] [--phi PHI]
[--a A] [--b B] [--objective {poisson,gaussian}]
[--prior_on_W {L1,L2}] [--prior_on_H {L1,L2}]
[--report_freq REPORT_FREQ]
[--active_thresh ACTIVE_THRESH] [--cut_norm CUT_NORM]
[--cut_diff CUT_DIFF]
input
signatureanalyzer input.maf -n 10 --reference cosmic2 --objective poisson
import signatureanalyzer as sa
# ---------------------
# RUN SIGNATURE ANALYZER
# ---------------------
# Run array of decompositions with mutational signature processing
sa.run_maf(PATH_TO_MAF, outdir='./ardnmf_output/', reference='cosmic2', hg_build='./ref/hg19.2bit', nruns=10)
# Run ARD-NMF algorithm standalone
sa.ardnmf(...)
# ---------------------
# LOADING RESULTS
# ---------------------
import pandas as pd
H = pd.read_hdf('nmf_output.h5', 'H')
W = pd.read_hdf('nmf_output.h5', 'W')
Hraw = pd.read_hdf('nmf_output.h5', 'Hraw')
Wraw = pd.read_hdf('nmf_output.h5', 'Wraw')
feature_signatures = pd.read_hdf('nmf_output.h5', 'signatures')
markers = pd.read_hdf('nmf_output.h5', 'markers')
cosine = pd.read_hdf('nmf_output.h5', 'cosine')
log = pd.read_hdf('nmf_output.h5', 'log')
# Output for each run may be found at...
Hrun1 = pd.read_hdf('nmf_output.h5', 'run1/H')
Wrun1 = pd.read_hdf('nmf_output.h5', 'run1/W')
# etc...
# Aggregate output information for each run
aggr = pd.read_hdf('nmf_output.h5', 'aggr')
# ---------------------
# PLOTTING
# ---------------------
sa.pl.marker_heatmap(...)
sa.pl.signature_barplot(...)
sa.pl.stacked_bar(...)
sa.pl.k_dist(...)
sa.pl.consensus_matrix(...)