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TCGA data(RPKM) differential gene expression

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!

rna-seq r tcga rpkm ipa
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written by realnewbie • realnewbie

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

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written by realnewbie • realnewbie

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.

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written by GenoMax GenoMax

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