Getting differentially expressed genes based on RPKM values

[casshyr]

Hello,
I have essentially 2 datasets, each showing the RPKM values for each gene. I want to compare the gene expressions from one set to another, and see which ones are up-regulated or down-regulated significantly comparing to the other.
I have tried some primitive ways, such as dividing the two by each other and see if that ratio is greater than a fold change threshold..but this yields to me like 10000 genes, which is unlikely.

Are there any suggestions on how to find differentially expressed genes based on RPKM values?

btw I don't have access to the mapped reads data, so programs like DEGseq won't work for me. I only have access to the RPKM values

Thanks!

*sorry in advance, but I've also double-posted this in the bioinformatics section, because I wasn't sure how this forum was organized (sry i'm new).

[litc]

Use Z-test

I think you can you Z-test to find the different expressed genes(DEG). you can also control the P value.

[RockChalkJayhawk]

Hello,

Are there any suggestions on how to find differentially expressed genes based on RPKM values?

btw I don't have access to the mapped reads data, so programs like DEGseq won't work for me. I only have access to the RPKM values

Your best bet would be to reverse calculate. If you have the transcript lengths, then you could factor that out and assume a standard read depth for all libraries, thus giving you an estimated raw tag count that you could then use as input for DEGSeq or DESeq.

Just a thought

[scottdaniel]

If the fpkm values are a result of cuffdiff then you can use the cummeRbund package in R to do your differential expression analysis