How to do functional analysis on differentially expressed gene list from RNA-seq data?
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13 months ago
Omurice ▴ 10

Hi all,

I am a complete beginner in terms of bioinformatics analysis and I am hoping to complete some functional analysis on some differentially expressed gene lists of some RNA seq data. However, I am a bit lost on how/best way to start: Below are the columns of the DE gene lists that I am operating upon (which seems to be quite different from other example data I’ve seen from various vignettes)

Ensembl Gene ID, RPKM of condtion 1, RPKM of condition 2, FDR 0.05, gene start, gene end, gene strand, gene name, gene description

Does anyone have any suggestions on how to import/modify this data into R so that will allow me to use a tutorial/vignette of sorts to perform GO or KEGG pathway analysis etc? (should/can I convert the RPKM to log2foldchange and pvalues? If so, how would I go about doing this?)

Thank you!

GSEA RNA-sequencing KEGG GO • 749 views
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13 months ago
MaxF ▴ 120

With the data you're describing it is not possible to do a robust/reliable analysis.

Step 1 is to determine the difference in gene expression between your two conditions. To do this properly you need to use a package like DESeq2, limma, or EdgeR. As far as I know, all of these algorithms require the raw counts (not RPKM, which is a normalized measure of abundance). Once you compare the two conditions, you follow the vignettes for something like ClusterProfileR.

https://bioconductor.org/packages/devel/bioc/vignettes/clusterProfiler/inst/doc/clusterProfiler.html https://brb.nci.nih.gov/BRB-ArrayTools/RPackagesAndManuals/GSEA-vignettes.html

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Hi. thank you for the reply. As far as I know the differential gene expression analysis was already completed and the output of the package used was in RPKM. This is what i am confused about; however as I do not know what is the best way to modify these values as an input for further functional analysis (would it be possible to calculate log2fold change from these RPKM values)?

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