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5.9 years ago
For getting up to speed quickly on statistical notions used everyday in computational biology, I suggest reading Modern Statistics for Modern Biology by Susan Holmes and Wolfgang Huber.
The book is based on the course of the same name taught by Susan Holmes at Stanford.
Kudos to Susan and Wolfgang - that is a very impressive resource.
Any feedback on this ? https://compgenomr.github.io/book/ If not im gonna continue with your reccomendation.
Modern Statistics For Modern Biology is more generic while Computational Genomics with R (the book you link to) is more directly targeted at genomics. Also modern statistics has more explanations and emphasizes some of the reasoning behind the math while I get the impression that computational genomics is lighter on explanations and is more written as a guide. So although the two books seem to overlap, they should be complementary. I'd suggest starting with Modern Biology and following up with Computational Genomics.
thank you. I'm indeed searching for something to get all main concepts + the ways to accomplish specific goals in R. I'd not like to go in deep into the mathematics behind. Do you also have an opinion on "Data Analysis for the Life Sciences with R" by Irizarry? I've taken a look at "Methods in Biostatistics with R" and it goes very much in mathematical details.