If I remember the circRNA literature correctly, the RPMs are used (and useful) to determine expression levels of circRNAs vs linear. It is a very specific case also because of the nature of circRNAs, the only reads used tend to be those of the splice junctions unique (or not) to the circRNAs. To be honest, I don't even remember which paper used RPMs other than to establish the expression of the linear form (if any).
With that out of the way, for DGE, use counts. It is the best method around, and it should work with circRNAs. Do look out for weird fit to the data and/or low counts which might happen when using backsplice reads only.
I already used DESeq analysis with raw counts but didnt give me logical results in my case as the gene IDs are non-conig cicRNAs not actual genes.
This is an issue of annotations, not of the DGE method. DGE tools are agnostic to the nature of the annotations - coding, non-coding, intergenic reads etc. You will need to overlap circRNA junctions with a gene annotation to get the host gene name, but this is independent.
The literature used RPM normalisation for differential expression
Could you please provide a reference?
I would advice not to do this, but use raw count data instead and go for limma or edgeR analysis.
I have to use mapping information thats why I am using RPM values
So you mean you don't have the original data?
I do but I have to use RPM as recommended by publications as my experiment is circRNA transcripts and I should normalise them to the mapped reads for a better analysis in this case
Then why didn't you say that from the beginning? We can't read your mind.
I thought it wouldn't matter :) sorry about that. a bit new in bioinformatics :(
It's better to add too much information than too little. Now you asked your question 6 hours ago, and we have just figured out what you are working on. Meanwhile, this question is going lower and lower on the forum, possibly escaping the view of people who can help you.
So I advise you to update your post, change the title to reflect "differential expression of circular RNAs". Don't forget to mention stuff like the organism you are working on, the number of samples, the data you have. Perhaps also add a reference to a publication suggesting you to use RPM.
Editing your first post will bump the post back to the top of the list, which is, in this case, convenient because you need some attention for your new information. (But don't abuse this feature...)
Thanks WouterDeCoster :)
What makes you think your approach is a valid approach to perform differential expression analysis? Unless you have good reasons, this bioconductor workflow is your starting point for every typical analysis.
I already used DESeq analysis with raw counts but didnt give me logical results in my case as the gene IDs are non-conig cicRNAs not actual genes. The literature used RPM normalisation for differential expression
Have you seen this answer by @Kanne? Additionally, what publication recommended RPM for DE, I'm curious? I'd generally agree with @WouterDeCoster that DESeq2's library size normalisation strategy makes sense.