signatureanalyzer 0.0.9

Bayesian NMF methods for mutational signature analysis & transcriptomic profiling on GPUs (Getz Lab).

SignatureAnalyzer

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.

Installation

PIP

pip3 install signatureanalyzer

or

Git Clone

• git clone --recursive https://github.com/broadinstitute/getzlab-SignatureAnalyzer.git
• cd getzlab-SignatureAnalyzer
• pip3 install -e .

Note --recurisve flag is required to clone submodules.

Docker

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

---

Source Publications

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)
  • see: https://github.com/broadinstitute/SignatureAnalyzer-GPU

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)

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Command Line Interface

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

Example:

signatureanalyzer input.maf -n 10 --reference cosmic2 --objective poisson

Python API

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(...)