Hi, Everyone, I am a real beginner in bioinformatics. I want to calculate differential expression profile of my TCGA data-mRNAseq(by using 5 normal, 5 tumor samples).However,to calculate this, I have RPKM values which requires non-parametric methods. Actually, I will upload this data to IPA(Ingenuity Pathway Analysis) tool to predict pathways,targets(for miRseq data from TCGA), upstream/downstream regulators. When I watch IPA tutorial, I realized that to predict all these from RNA-seq data, I need LogfoldChange value,dependent p-value and False DiscoveryRate. Unfortunately, I have not that much background how to deal with these calculations,how to calculate all of these from my RPKM valued TCGA data. Can anyone help me? Thanks a lot!
Have you looked at cBioPortal: http://www.cbioportal.org/
If you are only interested in looking up data this would be a painless way to do that.
IPA is a commercial software and you could contact their support on the input format required.
Anyway, a solution to getting DE analysis using TCGA is described here: How to work with Level 3 data (RPKM values) from TCGA database
In short, there is no accepted method to get DE-genes from RPKM, but it is possible to use the raw data.
Thanks a lot for explanation! Actually, I've watched all the videos, the format requires data-sets with deferentially expression signs,I mean the values like Log Fold Change, p-value,FDR and so on. My problem here is how to calcuate all these stuffs. I am a newbie, and I dont think that I have a strong background n coding. As far as I read, there are some recommendations: deSeq2,EdgeR,NoIseq and so on. However, I've been lost the information provided by the users of these packages. I have few samples for normal and tumor samples(having raw counts,median-length normalized and RPKM values of mRNA-seq data from TCGA and raw counts,read per million miRNa mapped count miR-seq data). What I want is to calculate differential expression of these by using R-package codes.I could not write the proper code. Many many thanks
I found this site very helpful,if you are interested in here it is: http://chagall.med.cornell.edu/RNASEQcourse/Intro2RNAseq.pdf