- Explain and interpret QC on count data using Principal Component Analysis (PCA) and hierarchical clustering
- Implement DESeq2 to obtain a list of significantly different genes
- Perform functional analysis on gene lists with R-based tools
Follow the instructions linked here to download R and RStudio + Install Packages from CRAN and Bioconductor
- Workflow (raw data to counts)
- Experimental design considerations
- Intro to DGE / setting up DGE analysis
- RNA-seq counts distribution
- Count normalization
- Sample-level QC (PCA and hierarchical clustering)
- Design formulas
- Hypothesis testing and multiple test correction
- Description of steps for DESeq2
- Wald test results
- Summarizing results and extracting significant gene lists
- Visualization
- Likelihood Ratio Test results
- Time course analysis
- Gene annotation
- Functional analysis - over-representation analysis
- Functional analysis - functional class scoring / GSEA
- DESeq2 vignette
- GitHub book on RNA-seq gene level analysis
- Bioconductor support site (posts tagged with
deseq2
) - Functional analysis visualization
These materials have been developed by members of the teaching team at the Harvard Chan Bioinformatics Core (HBC). These are open access materials distributed under the terms of the Creative Commons Attribution license (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.