This lesson provides a practical guide to sourcing and pre-processing a bulk RNA-Seq dataset for use in a machine learning classification task. The lessons explains the characteristics of a dataset required for this type of analysis, how to search for and download a dataset from each of the main public functional genomics repositories, and then provides guidelines on how to pre-process a dataset to make it machine learning ready, with detailed examples. The lesson finally explains some of the additional data filtering and transformation steps that will improve the performance of machine learning algorithms using RNA-Seq count data.
The lesson is written in the context of a supervised machine learning classification modelling task, where the goal is to construct a model that is able to differentiate two different disease states (e.g. disease vs. healthy control) based on the gene expression profile.
Prerequisites
This lesson assumes a working knowledge of programming in R. For learners who aren’t familiar with R or feel they need a refresher, the Programming with R provides a good introduction to both R and working with R studio.
