This repository contains all required code to reproduce the analyses for the stage-wise testing in RNA-Seq paper. The repository consists of two major folders: DGE (differential gene expression) and DTU_DTE (differential transcript usage, differential transcript expression), with the code to reproduce analyses from the respective applications. Both repositories contain a subfolder simulation and caseStudy.
The stage-wise analyses were performed with the stageR package. Development is hosted at the stageR GitHub repository
The simulations for the DGE analysis were based on the framework provided by the edgeR robust paper: X Zhou, H Lindsay and MD Robinson. Robustly detecting differential expression in RNA sequencing data using observation weights. Nucleic Acids Research 42 (11): e91
The original code for the differential gene expression simulation study can be found on their project’s website: http://imlspenticton.uzh.ch/robinson_lab/edgeR_robust/
In the DGE case study, we analysed the Hammer dataset. We downloaded the raw count table from the ReCount project website: http://bowtie-bio.sourceforge.net/recount/
In the introduction, we analyse the sim2_human simulated data from C Soneson, MI Love, MD Robinson: Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences [version 2; referees: 2 approved]. F1000Research 2016, 4:1521. Details and required files for this simulation study can be found at the supplementary data of that paper.
All other simulations for DTU or DTE analysis are based upon the framework provided by C Soneson*, KL Matthes*, M Nowicka, CW Law & MD Robinson: Differential transcript usage from RNA-seq data: isoform pre-filtering improves performance of count-based methods. Genome Biology 17:12 (2016).
The simulation study requires some files to be downloaded a priori and we therefore suggest to consult their GitHub repository (https://github.com/markrobinsonuzh/diff_splice_paper). Our repository is similar to their repository, with some adaptations to the bash scripts and R scripts. Details on the changes are reported in the paper.
The DTU case study considers the dataset provided by
S Ren*, Z Peng*, J Mao*, Y Yu, C Yin, X Gao, Z Cui, J Zhang, K Yi, W Xu, C Chen, F Wang, X Guo, J Lu, J Yang, M Wei, Z Tian, Y Guan, L Tang, C Xu, L Wang, X Gao, W Tian, J Wang, H Yang, J Wang and Y Sun. RNA-seq analysis of prostate cancer in the Chinese population identifies recurrent gene fusions, cancer-associated long noncoding RNAs and aberrant alternative splicings. Cell Research (2012) 22:806–821.
We downloaded the raw, unnormalized kallisto processed data from the Bear's lair project website (http://pachterlab.github.io/lair/).
Please contact koen.vandenberge@ugent.be or raise an issue on GitHub for questions.