Is There Any Paper That Compares/Combines Differentially Expressed Genes With Co-Expressed Genes?

Question

[Diwan]

Hi All,

I am interested to show that both differentially expressed genes as well as co-expressed genes are needed to get the complete picture of a disease. In my analysis, there is little overlap between these two sets of genes. I am looking for methods/ways/statistics to show that combination of these 2 list is better than using either one of them. Any suggestions?

So, I am looking for any paper that compares differentially expressed genes vs co-expressed genes. It will be helpful if you can point to such papers.

Thanks Diwan

Answer 1

[Charles Warden]

Off the top of my head, I can't think of a paper that shows exactly this.

However, I would probably say that the result will vary based upon what you are trying to do.

The best match that I can think of is what I have done with mRNA-miRNA interactions (unfortunately, none of which has been published yet). The list of genes with targets that have inverse overlap (e.g. miRNA Up, mRNA Down and miRNA Down, mRNA up) are usually fairly different than the genes that have negative correlations between miRNA and mRNA levels (paired for individual samples). You might expect that because you can see a negative correlation among miRNAs and mRNAs but not have the expression levels among the miRNAs and/or miRNAs vary between the two groups you were trying to study.

That said, the difference between overlap and correlation seems to be more similar with methylation and mRNA expression. My guess is that this is because the relationship is 1:1 for methylation:mRNA but not miRNA:mRNA. In other words, the importance of correlation for integration depends highly on the application. Nevertheless, I did show that there is a difference between overlap versus correlation in my COHCAP paper (I am just telling you that I happen to know the difference for miRNA-mRNA intergration is a lot greater):

http://nar.oxfordjournals.org/cgi/pmidlookup?view=long&pmid=23598999

If you are only working with mRNA, it is a little different story. However, the bottom line is that the tools serve different purposes. Just like the miRNAs, you might have co-expressed genes that don't vary between phenotypes of interest (such as tumor versus normal). I would probably argue that these sort of genes are generally not as interesting, but co-expressed genes will make up a subset of genes used for molecular profiling for subtypes. If you are trying to show that subtype analysis can complement differential expression analysis, you will see lots of evidence for that.

For example, here is one paper (among many) describing molecular subtypes with interesting clinical associations:

http://www.ncbi.nlm.nih.gov/pubmed/20129251

This paper quickly comes to mind because I recently published a paper studying the impact of these molecular subtypes on associations with a gene used for immunotherapy (or at least is in clinical trails for such an application):

http://www.ncbi.nlm.nih.gov/pubmed/24204956

Hope this helps!

[Diwan]

Thanks, Warden. I will check the paper that you mentioned. I am looking at affy data (mRNAs) only. I am looking at something like subtypes. For example, genes that are co-expressed in lung fibrosis but not in lung cancer etc. My goal is to identify genes relevant to lung fibrosis. Then to map those genes to PPI network. So I want to show that it is better to use differentially expressed genes as well as co-expressed genes.