https://github.com/gersteinlab/pcawgadditivevariance

https://github.com/gersteinlab/pcawgadditivevariance

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• Host: GitHub
• URL: https://github.com/gersteinlab/pcawgadditivevariance
• Owner: gersteinlab
• Created: 2018-05-06T17:07:03.000Z (about 7 years ago)
• Default Branch: master
• Last Pushed: 2018-05-06T17:30:25.000Z (about 7 years ago)
• Last Synced: 2025-01-20T12:07:44.082Z (4 months ago)
• Language: Matlab
• Size: 5.55 MB
• Stars: 1
• Watchers: 26
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# pcawgAdditiveVariance

This repository consist of code relevant for additive variance analysis performed on PCAWG mutations.

Following dependencies are required to run this workflow.

FunSeq2 (http://funseq2.gersteinlab.org/)

Python

Matlab

GCTA (http://cnsgenomics.com/software/gcta/#Overview)

This workflow consist of two components.

1) pre-processing step 

    In this step we generate summary file for each cancer cohort.

   

    *******************

    

    Two input files are needed for this step. 

    a) PCAWG driver mutation list and b)FunSeq2 output file(in BED format) 

    

    Usage:

    generateSummaryInfo.py -d  -I  -O 

    generateSummaryInfo.py (-h | --help)

2) post-processing steps

   Pipeline is run by calling additive_variance_demo.m

   *******************

   Full pipeline requires the following inputs:

    - in bedFiles folder:

      cohortName.null.bed

      cohortName.obs.bed

   - in summaryFiles folder:

     cohortName.null.summary.txt

     cohortName.obs.summary.txt

   and generates the output cohortName.txt in the results folder.

  A gcta executable is required (Linux version is included, Windows and Mac versions are available from cnsgenomics.com/software/gcta), which should be placed in the gctaFiles folder.

*******************

Current pipeline is set up to call only final stage, which summarizes the results from precomputed intermediate outputs.

Outputs are computed from the Breast-AdenoCa cohort with randomized samples used for both null and obs conditions.

Results text file shows calculated additive variance for each funseq threshold, which is ~0 (1e-6), along with associated p-values (0.5 indicates that no significant genetic variance was found).

Values of -1 in the results file for funseq thresholds 5 and 6 indicate that insufficient data was found at these thresholds.