Hello All
I had planned to carry out 450K human methylation data analysis and gene expression analysis to corelate differentially expressed genes to differentially methylated regions for publicly available cancer data from TCGA.
I had used COHCAP to find DMRs in 75 samples (65 T + 10 N).[450 K human methylation data level 1 tcga]
Next I used edgeR to find differentially expressed genes in 68 samples (65T + 3N) (RNA-seq data was missing for 7 N samples) [rnaseqv2 data level3 tcga]
For a proof of concept, I extracted 458 genes from CpG_island_filtered-Avg-by_Island.xlsx (from COHCAP analysis) and only 36 downregulated genes from edgeR analysis.
Only 2 genes were common in both the lists. I am worried if I am missing some important steps as I had expected at least 20 - 30 genes that would be commonly related in both the COHCAP analysis(450K methylation array level 1) and edgeR analysis (rnaseqv2 level 3).
Any suggestions or improvements are welcome!
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arpit.singh1203 •
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Are the 65 tumor samples matched in the methylation and expression data? If so, why not simply perform a correlation instead of trying to compare gene sets?
Yes the 65 samples (Tumor) are matched with unique tcga ID. I am afraid I don't understand what you mean by co-relating rather than comparing. Could you please discuss a little more on that?
Compute the correlation between the expression and methylation for each gene directly using pearson correlation, for example.
I ran DESeq2 on my data and got zero DE genes. I think the following reasons could cause this:
Do you think these could be the reasons? And is that why you suggested me to "compute correlation between the expression and methylation for each gene directly using pearson correlation"?
PS: All rows containing zeroes were removed.