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Old 04-26-2017, 05:44 AM   #3
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Location: Bielefeld

Join Date: Nov 2015
Posts: 4

The focus of my study is on fold change(FC)-based evaluation of transcriptional changes, thus intra-gene comparison. I am interested in genes which are strongly differentially regulated between two conditions. However, I also do some inter-gene comparison, such as comparing the transcript abundance of transcript A with transcript B, in order to determine which transcript is more abundant. For instance, if transcript A is up-regulated 10-fold, but from FPKM 1 to FPKM 10, yet transcript B is up-regulated 3-fold from FPKM 1000 to FPKM 3000, I would conclude that the up-regulation of transcript B might be metabolically more relevant. However, for most cases, I stick only to FC-values.

My key concern is that the use of TPM is advocated over FPKM. FPKM can be easily converted to TPM by dividing the each FPKM value by the sum of all FPKM values of the respective sample, and multiplying this by 1e6. This yields TPM.

However, Cufflinks internally has complicated statistics to compute FPKM values. Therefore I wonder if I violate any rules by converting FPKM to TPM? Does this particularily apply as I ran Cuffnorm, which already performs a normalization?

What do you recommend? Stick to Cuffnorm's FPKM values? Or perform the simple FPKM-to-TPM conversion from above and use TPM values instead?
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