Hey all~
I am not new to bioinformatics....but I am very new in analyzing RNA-seq data set. I work in a pure wet lab, and I am the only part that is partially dry....
Usually what I do is calculate the gene expression using HTseq and cufflink after Tophat and Bowtie transcript assembling. The unusual part is I never run differentially expressed genes using edgeR or cuffdiff. After the gene expression calculation, I just work on the raw reads, eliminate the genes that get too few reads, and ignore the genes that have a too small change in quantity. Then I will run t test on all the genes that meet the requirement and eliminate the data groups that has a p value from t-test that is bigger than 0.05. Obviously I don't get RFP, but could anyone tell me which way (common way or my way) could lead to more reliable result(s)?
You might see this is too simple.... even naive.... but could anyone of you tell me is that going to be very wrong by doing what I did?
Thanks~
Y.L.
I am not new to bioinformatics....but I am very new in analyzing RNA-seq data set. I work in a pure wet lab, and I am the only part that is partially dry....
Usually what I do is calculate the gene expression using HTseq and cufflink after Tophat and Bowtie transcript assembling. The unusual part is I never run differentially expressed genes using edgeR or cuffdiff. After the gene expression calculation, I just work on the raw reads, eliminate the genes that get too few reads, and ignore the genes that have a too small change in quantity. Then I will run t test on all the genes that meet the requirement and eliminate the data groups that has a p value from t-test that is bigger than 0.05. Obviously I don't get RFP, but could anyone tell me which way (common way or my way) could lead to more reliable result(s)?
You might see this is too simple.... even naive.... but could anyone of you tell me is that going to be very wrong by doing what I did?
Thanks~
Y.L.
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