Hi all,
I want to rank gene expression based on the transcript abundance to see the relative expressions of different genes in my RNA-seq sample. I ran Cufflinks for 3 biological replicates for that condition to get the FPKM values. However, the FPKM values are very different within the 3 biological replicates, which I am not sure why and how to deal with this.
In Cufflinks manual, It said that Cufflinks can only use classic-fpkm normalization on one library at a time. Should I use Cuffdiff2 to normalize FPKMs and fragment counts across all libraries (This policy is identical to the one used in DESeq) and take the average of these normalized RPKMs?. Or do you think it is ok to get the FPKMs from biological replicates produced by Cufflinks and feed them into DESeq to normalize them?
Thanks for your opinions.
Thanh
I want to rank gene expression based on the transcript abundance to see the relative expressions of different genes in my RNA-seq sample. I ran Cufflinks for 3 biological replicates for that condition to get the FPKM values. However, the FPKM values are very different within the 3 biological replicates, which I am not sure why and how to deal with this.
In Cufflinks manual, It said that Cufflinks can only use classic-fpkm normalization on one library at a time. Should I use Cuffdiff2 to normalize FPKMs and fragment counts across all libraries (This policy is identical to the one used in DESeq) and take the average of these normalized RPKMs?. Or do you think it is ok to get the FPKMs from biological replicates produced by Cufflinks and feed them into DESeq to normalize them?
Thanks for your opinions.
Thanh
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