Question: Is it possible to use edgeR and Limma for meta analysis?
0
gravatar for Pas
3.7 years ago by
Pas20
Milano
Pas20 wrote:

Hi all,

I was wondering if it is possible to use edgeR and Limma to perform meta-analysis of gene expression data generated from different platforms. In particular I'd use the generalized linear model GLM  and  a design  model  where I can adjust for differences between the different platforms by using an additive model formula like this:

design <- model.matrix(~platform+Treatment)

 

What do you think?

 

 

ADD COMMENTlink modified 3.7 years ago by andrew.j.skelton735.7k • written 3.7 years ago by Pas20
1
gravatar for andrew.j.skelton73
3.7 years ago by
London
andrew.j.skelton735.7k wrote:

While this is possible, there's a lot of caveats to it... You'll need the same sample types on both platforms (to estimate within platform variance). If you're using microarrays, you'll have to match probes that are the same up (note, that these would have to be pretty damn close, i.e. mapped up nuIDs so that they have the same nucleotide sequences) - There are packages like inSilicoMerging. Either way, you generally need to take this with a pinch of salt.

If you're using doing this for RNA Seq, I'd recommend using DESeq2 for gene level and Sleuth for Transcript level - Both can handle additive models. If you're using microarrays, use Limma. 

ADD COMMENTlink written 3.7 years ago by andrew.j.skelton735.7k

Thank you Andrew.

I agree with you. I heard that people are trying to use egdeR or limma to perform a meta analysis integrating both microarray and RNAseq, specifying the  platform  in the design model, and since I 'm not sure that is possible, I'd like to have more opinions.

 I have many concerns about  this approach. For example, what about  the normalization? Microarray data cannot be normalized like RNASeq data, and edgeR needs  raw data (not normalized) as input. Then, giving to edgeR a matrix containing raw data from both RNAseq and microarray it'd be a error. Do you agree?

ADD REPLYlink modified 3.7 years ago • written 3.7 years ago by Pas20
1

Woah, while I'm saying it's possible with the same technology (i.e. 2 microarrays combined, or two RNA Seq datasets combined), I have to highly recommend against combining 2 different technologies (i.e. microarray and RNA Seq dataset). There are ways to look for correlations between microarrays and RNA Seq data, but combining for differential expression analyses and such would be more trouble than it's worth going down that rabbit hole. 

ADD REPLYlink written 3.7 years ago by andrew.j.skelton735.7k
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