Hi all,

I'm a bioinformatician and I often have to deal with RNAseq differential gene expression analysis projects.

I think I understand well the whole process to get from the raw data to the the normalized read counts but, unfortunately, due to my little statistical background, I'm having trouble dealing with the last step of differential expression.

When it comes to simple pairwise comparison between two conditions I understand the process, but when there is more complex comparisons (timelapses, multiple comparisons, including confounding effect ... ) I'm struggling for chosing the relevant design matrices.

I'm curious if anyone would know good tutorials, online courses, books, or any ressources that would allow me to learn how to get better at that.

Thank for your help,
I've not actually done it myself, but people say good things about Rafael Irizarry's courses. Particularly see weeks 3 and 4 of the Introduction to Linear Models and Matrix Algebra course [here][1] and [chapter 5][2] of his book. 


  [1]: http://rafalab.github.io/pages/harvardx.html
  [2]: http://genomicsclass.github.io/book/