Creating Teaching Tools for RNA‐Seq Analysis in R

Abstract

RNAseq is a popular method that has become standard in recent years but it requires significant computational skills to analyze. R is a valuable and effective tool that is commonly used for big data analysis because it is free and open source. Unfortunately, it is also fairly complex and often overwhelming for those lacking a computational background which creates a barrier to many looking to effectively analyze transcriptomic data. This project's objective was to compile a series of instructional materials useful for individuals hoping to better understand and run aspects of an RNA-Sequencing pipeline. We utilized an RNA-Seq dataset from a study on transcriptomic changes that occur during ex vivo machine perfusion of human livers as our sample dataset to design teaching tools for R. We created and documented a pipeline for statistical analyses of RNA-Seq data to generate a suite of publication quality graphics. This included teaching materials for users across a broad range of experience, ranging from those with no exposure to R to the more experienced. These teaching tools include: videos for beginners, comprehensive instructional files available through pdf documents and GitHub, and user guides specific to R scripts which generate publication-standard figures and graphs. We also created general code and tutorials that can be utilized for other large omics datasets. We hope that by providing instruction through various media types, using the R platform for big data analysis will become more approachable and user-friendly.