So,there is a project I am working on with somebody for analyzing deferentially expressed genes from couple of array/RNA seq data sets. Seems like people are interested in only checking up the expression profile of only two genes among different comparison groups in all of the data sets. Just to be clear i used a straightforward limma based deferentially expressed gene analysis.

So at the end we have these fold change values along with the t , p and adjusted p values (for multiple comparisons) for each datasets and the comparison that we performed.

Now we take out just these statistics for these two genes and use them for further analysis.

According to my understanding we should use adjusted p values for any of the further analysis because we used limma and it already used moderated t test where the variances of all genes are considered in order to generate the final t p and adj p values. plus the fold change is a intensity based relative measure and we can not kind of compare this with RT-pcr or simple pcr based analysis type of thing. what i mean to say is people want to use p values and i am not sure about it. Their argument is that when we use RTpcr or pcr based low throughput techniques we usually end up analyzing just bunch of molecules and we dont use ofcourse adj p values and since we are interested in only two genes from array analysis so we should use p values instead of adjusted p values?

Any possible explanation or opinion on this (positive, negative, anything) ?

Thank you.

Here are some people's thoughts (which I generally agree with): http://seqanswers.com/forums/showthread.php?t=48011

I quote: "You cannot simply adjust the p-values from the original analysis as those p-values were based on a variance model from the whole transcriptome data set. Your small subset of genes may represent a very different set of data than the original complete data set."

Here are my own thoughts from a previous post I made on biostars (if you're considering using a standard non-limma-based Student's t-test on your microarray data): C: p-value in Limma vs Graphpad

In essence, as has been stated by other comments, use adjusted p-values if you're going off your limma analysis. Also, think about what do you really want to gain from looking at a (adjusted) p-value. People think that p-values are the "end all be all" (they're not; all they do is tell you if there's enough evidence against the null hypothesis).

It is very unclear what you mean here. Can you clarify how t-statistics, p and adjusted p values are used for "further analysis"?

Yeah so further analysis is nothing more than that these statistics are coherent with the hypothesis they were testing coming up as significant for related metabolites modulated by related genes(coming from microarray) of similar comparisons performed separately and we are gona see them in plots!