Scripts supporting identification of genomic features affecting survival time in cancer
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cbioportal
cnv-and-mutation
common
copy_number
drug-sensitivity
fdr
high-confidence-biomarkers
histological-type elife-revisions Sep 24, 2018
icgc
make-pancan
mskcc elife-revisions Sep 24, 2018
mutation_analysis
structural-breaks
tumor-purity
tumor-stage
.gitignore initial commit Aug 8, 2017
LICENSE
README.md
requirements.txt
run.py

README.md

genomic-features-survival

Scripts supporting identification of genomic features affecting survival time in cancer.

Getting Started

The easiest way to get started is to use requirements.txt to set up a conda environment with the relevant packages.

$ conda create --name <env> --file requirements.txt

The code in this repo was built with python 2.7, pandas 0.18, numpy 1.10, and scipy 1.0.0 (as is captured in requirements.txt)

The source data is stored in a public GCS bucket. Documentation for accessing public GCS data is here. The bucket for this project is called public-smith-sheltzer-cancer-analysis.

Use run.py to download the relevant data from the public GCS bucket and perform univariate analyses for copy number and mutation. run.py takes two optional arguments -- the folder to store data and analysis, (default .) and the number of parallel workers to use for the analysis (default none, and a sequential only analysis.). With -p 4, run.py takes ~12 hours on a 2017 MacBook Pro.

$ python run.py -p 4 -o $ouput_directory

Organization

  • cbioportal - scripts used to analyze cbioportal data
  • cnv-and-mutations - scripts for analyzing with cnas and and mutations together
  • common-case-zscores - allows getting zscores for every row in a "common" file. common files have genes in rows, patients in columns.
  • common - the common set of utilities and tools used in analysis.
    • analysis.py has the meat of cox analysis
    • mutation_base.py has the repeatable processing required to turn raw mutation data into usable dataframes.
  • copy-number-analysis - given a copy number file, data about gene/location, and TCGA clinical data, calculate zscores for copy number genes
    • process_copy_numbers_to_genes.py has the repeatable processing to turn copy number raw data into usable dataframes.
  • data-munging - contains scripts for miscellaneous small processing tasks: one-off zscores, density plot generation, etc
  • fdr - scripts for performing false discovery correction
  • geo - scripts for analyzing zscores for GEO files
  • make-pancan - given a filetype/platform, take all the per-cancer-type zscore files and produce a file with genes in rows, and cancer types in columns
  • mutation-analysis - given a tcga clinical file, and mutation data from the same set of patients, calculate zscores and kaplan meier curves for genes mutated in a sufficient number of patients
  • pan-platform - scripts for creating panplatform TCGA files