Differential Expression Analysis Theory/Tutorial For The Statistics Layman

Question

[Travis]

Hi all,

I am wondering if anyone can recommend a good resource to learn/refresh on the basics of differential expression analysis and related statistical tests and inference. I covered this stuff a long time ago but never really had to use it and when I now start reading a paper on RNA-Seq analysis for example, I get a headache as soon as the statistics start to be discussed.

Thanks in advance.

Answer 1

[Ryan Dale]

I've found that reading documentation for a particular package can be helpful when read in parallel with the original paper. Bioconductor packages are typically great for this.

For example, the vignette for DESeq and the vignette for the development version of DESeq do a good job of using specific examples to demonstrate the process outlined in the paper.

Another benefit to this is that if you type the commands as you follow along, you can explore the data structures on your own (in this case, in R) to get a more intuitive feel of what's going on

[Travis]

Thanks for the advice, however I am interested in finding a general overview of the main considerations and classes of analyses (and why they are used), rather than individual packages themselves.

[Travis]

Thanks for the advice, however I am interested in finding a general overview of the main considerations and classes of analyses (and why they are used), rather than individual packages themselves.

Answer 2

[Lee Zamparo]

Hi Travis,

I've found that this paper by Storey and Tibshirani is helpful; it's written convincingly, and has examples of how different types of data can be analysed. It's a good start.

Answer 3

[Rm]

Microarray Expression Data Analysis References

Course slides: Statistics for Gene Expression

Differential Expression Analysis - of Microarray Data

Answer 4

[Travis]

This seems like it might be a good introduction for those of us who have forgotten (or never learned) stats.

Answer 5

[Khader Shameer]

I would recommend this book:

"Microarrays For An Integrative Genomics" by Kohane, Kho and Butte.

Also see a related discussion here.