DESeq2

• Vignette
• Tutorial

On filtering low counts and DE analysis of low-expressed genes

• Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences. Bioinformatics, 2019.
• Inferential considerations for low-count RNA-seq transcripts: a case study on the dominant prairie grass Andropogon gerardii. BMC Genomics, 2016.
• consensusDE: an R package for assessing consensus of multiple RNA-seq algorithms with RUV correction. PeerJ. 2019.

Adjusted p-value and multiple testing

• Why, When and How to Adjust Your P Values? Cell J, 2019.
• How does multiple testing correction work? Nat Biotechnol, 2009.

Online course materials

• https://github.com/hbctraining/rnaseq_overview
• http://chagall.med.cornell.edu/RNASEQcourse/
• https://galaxyproject.org/tutorials/rb_rnaseq/

References

• A survey of best practices for RNA-seq data analysis. Conesa et al., Genome Biology, 2016.
• Wang Z, Gerstein M, Snyder M. RNA-Seq: a revolutionary tool for transcriptomics. Nat Rev Genet. 2009 Jan;10(1):57-63. doi: 10.1038/nrg2484.