Matthew J. Oldach
- E-mail: [email protected]
- Twitter: @MattOldach
- Website: https://moldach.github.io/
Often times it will be difficult to pull out any biological understanding from a small list of differentially expressed transcripts/proteins/metabolites/etc.
This repo covers techniques for the analysis of gene set enrichments, pathway analysis, gene ontologies, functional analysis of metabolomic profiling and coexpression networks.
These scripts cover statistical problems and solutions in functional analysis of high-throughput data and gives an overview of commonly used functional analysis techniques (GSEA, gene ontologies, MSigDB, tmod, metabolic profiling) as well as multivariate techniques and machine learning.
This repo has several sections
- Data preparation (TO DO: COVER IN MORE DETAIL)
Please read CONTRIBUTING.md for details on our code of conduct, and the process for submitting pull requests to us.
This code is released under the MIT License - see the LICENSE.md file for details.
- This is material developed by Dr. January Weiner from Max Planck Institute for Infection Biology, GER
- The material herein was covered at the 5 day workshop in Berlin March 12-16 2018, Squeezing biology out of statistics: Gene set and pathway analysis in HT data held by Physalia Courses