Class Date Topic
1 9/30 Introduction to RNAseq data and technology (slides)
2 10/14 Setting up your software environment: an introduction to R, RStudio, and Git (slides)
3 10/21 Version control with Git, and GitHub (slides)
4 10/28 Understanding normalization and variance in RNAseq data (slides)
    * Read alignment and summarization
    * Normalization and sources of variation
    * Looking to the literature for cases where they got it wrong
5 11/04 Starting your analysis script
    * defining your experiment in a simple study design file
    * raw data and normalization
    * annotation
6 11/11 Exploratory analysis of expression data (gene-agnostic)
    * viewing sample relationships with dist and hclust functions
    * using the prcomp function to carry out a principal component analysis
    * using ggplot2 to visualize your PCA analysis
    * using PCA ‘loadings’ to examine the relationship between samples and principal components
7 11/18 Open Working Lab - exploring your own data
8 11/24 Managing and tidying data in R
    * using dplyr’s mutate, sort and filter commands to edit data tables
    * static and interactive scatter plots using ggplot2 and ggvis
    * melting data with the reshape2 package
9 12/02 Identifying differentially expressed genes
    * using the Limma package
    * P values, false discovery rates, and volcano plots
10 12/09 Visualizing and dissecting your differentially expressed gene list
11 12/16 Understanding and leveraging Gene Ontology for functional enrichment analyses (slides)
12 01/06 Gene Set Enrichment Analysis (GSEA) and gene signatures
13 01/13 Gene Set Enrichment Analysis-part II
14 01/20 Open lab exercise - work on analysis summary figure
15 01/27 Making your anaysis pipeline transparent and reproducible using R Markdown and Knitr (slides)
16 02/03 Rmarkdown wrap-up
17 02/10 Deploying your data to the web using Shiny
18 02/17 Final figure, Rmarkdown report, and shiny web app due