[BioC] Heatmaps for EdgeR

[Steve Lianoglou]

Hi,

On 21 Mar 2014, at 11:39, Eleanor Su wrote:

> Hi Steve,
>
>> Don't take this the wrong way, but it sounds like you are quite new 
>> to not
>> just this analysis, but to R as a whole since indexing things 
>> (vectors,
>> lists, matrices) is something very basic that you need to master 
>> before
>> being conversant with the language.
>
> Indeed, I have limited knowledge in using R and edgeR. Thanks for the
> suggestion to contacting R-help for these questions. Unfortunately my
> graduate program offers very little help with R statistics and even 
> fewer
> with bioinformatics especially that of small RNAs. With these limited
> resources, I feel like I'm working in the dark and my analysis, to say 
> the
> least, is cryptic. I'll take a step back before I jump the gun with 
> the
> analysis. Thanks for the insight.

This exact issue has been making its rounds on the internet due to this 
recent blogpost:

http://biomickwatson.wordpress.com/2014/03/20/is-this-a-realistic-portrait-of-a-modern-studentpost-doc-in-biology/

So you are not alone ... but rest assured that many of us are here to 
help (and happy to do so ;-)

Your analysis is on the right track. You should follow along with the 
examples in the edgeR (or even the limma (for limma::voom and its 
extensive linear modeling material)) user's guide(s) to get an idea of 
how to setup analyses for differential expression. Both of these manuals 
are very thorough and great to just digest and understand (be sure to 
read the relevant primary publications, as well). You should also take a 
look at the DESeq2 vignette, as similar material is presented there and 
perhaps this (third) treatment of the material might help it all to 
click.

The fact that you are working with small RNAs doesn't change the picture 
*too much* for the "simple" differential expression stage of the game 
(putting mapping issues aside, for small molecules).

Lastly, and this is important, you are also fortunate to be "in 
training" during the era of MOOCs. Coursera has a data analysis "track" 
that covers many things that will be relevant to you:

https://www.coursera.org/specialization/jhudatascience/1

(and other courses of interest):

https://www.coursera.org/jhu

And ESPECIALLY take note of this class that is starting shortly:

Data Analysis for Genomics
https://www.edx.org/course/harvardx/harvardx-ph525x-data-analysis-genomics-1401

Don't miss it!

The material is exactly the type of stuff that you need to know, and as 
a special treat, is taught by top-notch instructors. I'm planning to 
audit the class, and I (should ;-) know most of this stuff already!

HTH,
-steve

--
Steve Lianoglou
Computational Biologist
Genentech