(0.25 credits)

1. RNA-seq experiments: from theory to costing. High throughput sequencing, an introduction to data types in high throughput sequencing. Designing HTS experiments for maximum power and minimum cost.
2. PCA, compositional biplots, and correlations
3. Introduction to the Barton RNA-seq dataset. Initial exploration of the data using PCA. Finding outliers. Implications for experimental design.
4. Removing outliers. Count normalization and data transformations. Why, how, when.
5. Differential abundance using edgeR, DESeq2 and ALDEx2. Comparing and contrasting the results.
6. Pathway analysis of differential abundance results (KEGG, GO)
7. Generalizing to microbiome, metatranscriptome and other data types.