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Using supervised learning methods for gene selection in RNA-Seq case-control studies

This git repository contains the additional files used in the following research article: https://www.frontiersin.org/articles/10.3389/fgene.2018.00297/full

Pre-requisites

  • TCGAbiolinks
  • dplyr
  • DT
  • DESeq2
  • stringr
  • ranger
  • caret
  • survival

Running the code

The source code is currently split into different R source files, corresponding to the following steps:

  1. Data download from TCGA, save HTSeq-count files for all samples
  2. Run DESeq2 (normalization and differential expression analysis)
  3. Run random forests
  4. Survival analysis

Extreme Pseudo-Samples

The EPS method used to extract the other ranking of genes is run in parallel of step 3. The source code for the EPS method is available at: https://github.com/roohy/Extreme-Pseudo-Sampler/

Please note that

  • Some variables may have to be modified in the R scripts (eg. baseDir) according to your system configuration.
  • These R scripts rely heavily on external libraries which might be subject to change (especially TCGAbiolinks). If that were the case, some errors might occur.

Citation

Wenric S and Shemirani R (2018) Using Supervised Learning Methods for Gene Selection in RNA-Seq Case-Control Studies. Front. Genet. 9:297. doi: 10.3389/fgene.2018.00297