Last updated: 2016-08-16
Code version: e0856a352e494c0de5f1f93226e04753d2aebdca
A broad overview of time course gene expression experiments, from experimental design concerns, computational workflow, to statistical analysis needed for time course experiments. Only a paragraph on differential gene expression analysis. Recommended for learning experimental context of analysis concerns in time course experiments. Bar-Joseph et al 2012
A critical review that focuses on differential expression analysis of time course gene expression data, specifically RNA-seq data, and that identifies contemporary methods and their limitations in addressing biological questions unique to time course experiments. Recommended for a brief and targeted overview. Oh et al 2014
A critical review that analyzes computational and statistical challenges of contemporary methods for differential expression analysis of RNA-seq time course experiment data. Spies et al 2015
sessionInfo()R Under development (unstable) (2016-03-11 r70310)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.10.5 (Yosemite)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] knitr_1.14
loaded via a namespace (and not attached):
[1] magrittr_1.5 formatR_1.4 tools_3.3.0 htmltools_0.3.5
[5] yaml_2.1.13 Rcpp_0.12.6 stringi_1.1.1 rmarkdown_1.0
[9] stringr_1.0.0 digest_0.6.10 evaluate_0.9