Linear Models for Differential Abundance Analysis

Linear models are routinely used to perform differential abundance analysis in various ‘omics applications, including microarrays, RNA-seq, and proteomics. In this virtual learning experience (vLE) we will introduce linear models from the ground up and demonstrate how to use them to perform differential abundance analysis in a range of experimental designs of increasing complexity. We will use the F1000 Research article “A guide to creating design matrices for gene expression experiments” (https://f1000research.com/articles/9-1444) as our guide. While no prior experience is assumed, if you would like to code along, you will need to have the programming language R installed. The code for this session can be found at https://github.com/BAREJAA/vle_code/blob/main/vle_11_14_2022.Rmd We welcome your feedback! Please complete this survey after the session. If you leave your email, you'll be entered in a drawing to win a gift at the end of the semester. We welcome your feedback! https://duke.qualtrics.com/jfe/form/SV_eboL5WdfBxkfuKO?EventID=221114Bareja