Hello,
I am doing a DE analysis on TCGA BRCA data.
In particular, I took 85 tumor-normal pairs from the data and am trying to figure out which genes are differentially expressed.
And surprisingly, I found that at least 9,000 out of 20,000 genes are.
So my question is if my analysis with this radical number can be right, and if not, what's wrong in the process.
To this end, I am employing the step 14 suggested in S. Anders et al. in Nat. Biotech. 2013 (Count-based differential expression analysis of RNA
sequencing data using R and Bioconductor), especially for pre-processing steps. Since DESeq2 is not mentioned, I did the analysis only with edgeR. Attached are the figures from plotMDS,plotMeanVar,plotBCS, and plotSmear functions according to the suggestion. I think they imply my analysis can be right, in the sense that many tagwise variations are less than common variation and the cases are separated quite well in MDS.
Interestingly, my another DE analysis using a widely-known non-parametric tool, SAMSeq, suggests around similar number of DE genes.
Again, can my analysis be OK? but I have never seen this kind of case with this number of DE genes.
So if my analysis is not correct, can you please tell me what's wrong?
Thanks in advance.
I am doing a DE analysis on TCGA BRCA data.
In particular, I took 85 tumor-normal pairs from the data and am trying to figure out which genes are differentially expressed.
And surprisingly, I found that at least 9,000 out of 20,000 genes are.
So my question is if my analysis with this radical number can be right, and if not, what's wrong in the process.
To this end, I am employing the step 14 suggested in S. Anders et al. in Nat. Biotech. 2013 (Count-based differential expression analysis of RNA
sequencing data using R and Bioconductor), especially for pre-processing steps. Since DESeq2 is not mentioned, I did the analysis only with edgeR. Attached are the figures from plotMDS,plotMeanVar,plotBCS, and plotSmear functions according to the suggestion. I think they imply my analysis can be right, in the sense that many tagwise variations are less than common variation and the cases are separated quite well in MDS.
Interestingly, my another DE analysis using a widely-known non-parametric tool, SAMSeq, suggests around similar number of DE genes.
Again, can my analysis be OK? but I have never seen this kind of case with this number of DE genes.
So if my analysis is not correct, can you please tell me what's wrong?
Thanks in advance.
Comment