Hi all,
I am doing GSEA for RNA-Seq data and have results.I want to look at the protein expression levels for correlation,which I have downloaded from TCGA for same disease type.for e.g if there is some pathway which is up-regulated in GSEA ,what are the protein expression levels for that?Is there any method anyone could suggest?
How should I do the comparison of protein expression vs gene-expression?
1 answer
We are currently working on this with our application, iPathwayGuide which can take data from both the proteome and transcriptome and perform a meta-analysis that shows the overlap. We have collaborations with both leaders in RNA-Seq and Mass-spec to generate these data.
Will keep you informed.
Thanks for the question.
It's completely free for all to try. There is no limit to the number of datasets you can upload and analyze. The output is available for 72 hours without restriction (except the ability to download the graphs/tables). Reports can be purchased if you need them beyond the 72 hours which also unlocks the download function. We also offer subscriptions with deep discounts for academics/non-profits.
Log in to answer this question.
-
Finding RNA genes associated with X protein expression in CITE-seq Seurat object
written by Picasa
-
How Can Pathway Analysis Methods Account for Genes with Different Activation Roles?
written by RoyBatty •
-
RNA seq normalization and gene gene correlation
written by Fish •
-
Pathways enriched across multiple transcriptomics datasets?
written by kakukeshi
-
Gene Set Enrichment Analysis with RNA-seq data
written by deepak18 •
-
How to get wikipathways without disease and cancer ontologies?
written by Gabriel
-
Normalizing RPPA data counts from TCGA
written by vinayjrao
-
Ratio of samples to do differential expression
written by Ron
-
Interpreting GSEA results
written by Ron
-
TCGA microarray comparison with RNA-seq
written by Vasei •
Then you would also be needed to have Mass-spec data on proteome in that particular lines to quantify proteins.
Level of correlation is always quite less because different isoforms are translated at different levels. There are so many transcripts which doesn't get translated at all or they undergo nonsense mediated decay.
hth