Hi all,
I'm a PhD student newbie to statistical analysis in RNAseq. I got an experimental design and I'm trying to find a good way to approach the analysis of the results.
Basically, I have 5 different fruit varieties harvested at 4 different time points that have been RNAsequenced (so far with no replicas, don't know if we'll get a second shot). I was asked to find those genes showing a different behavior in the 5 varieties both in terms of differential expression on the respective time points and in terms of time course. I thought to use DESeq with the "blind" method (according to the vignette) by making a pair-wise comparison of one of such varieties (and in one time point) with the others (and the other time points, within the same, repeated for the 5).
The problem is, that I am not sure whether this is the most straight-forward way to approach this problem and, moreover, it is still not so clear to me how to represent the results (if making a mega-merge or so).
Does anyone have/can give me a hint?
many thanks
Marco
I'm a PhD student newbie to statistical analysis in RNAseq. I got an experimental design and I'm trying to find a good way to approach the analysis of the results.
Basically, I have 5 different fruit varieties harvested at 4 different time points that have been RNAsequenced (so far with no replicas, don't know if we'll get a second shot). I was asked to find those genes showing a different behavior in the 5 varieties both in terms of differential expression on the respective time points and in terms of time course. I thought to use DESeq with the "blind" method (according to the vignette) by making a pair-wise comparison of one of such varieties (and in one time point) with the others (and the other time points, within the same, repeated for the 5).
The problem is, that I am not sure whether this is the most straight-forward way to approach this problem and, moreover, it is still not so clear to me how to represent the results (if making a mega-merge or so).
Does anyone have/can give me a hint?
many thanks
Marco
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