A common task when working with transcriptomic data is the identification of differentially expressed (DE) genes or tags between groups. In this workshop participants will learn how to perform biostatistical analysis in the R programming language, including pairwise and analysis of variance (ANOVA) like comparisons to identify significantly DE genes.
The analysis described in this workshop is illustrated in the Bioinformatics Analysis of Omics Data with the Shell & R tutorial and is downstream of the tutorial on Bioinformatics Analysis of Omics Data with the Shell & R, in which a walkthrough is provided of a simple bioinformatics workflow for quantifying transcriptomic data.
- Be able to replicate the statistical analysis of a published biological data set.
- Become comfortable working with quantified transcriptomic data.
- Learn what statistical comparisons are possible with the design of the experiment.
- Be able to perform pairwise comparisons with edgeR.
- Discover different types of plots for exploring expression data and analysis results.
- This workshop is designed for participants who are unfamiliar with statistical analysis.
- Participants are expected to be comfortable working in the R programming language.