Hi everyone,
I'm very new to the world of RNA-seq so forgive me for my lack of knowledge.
I got some RNA-seq data from my control cells and treated cells, with 3 replicates of each. The company that did the sequencing provided a bunch of BAM files from aligning the data to the mouse genome with TopHat.
It was suggested to me that I should use DEseq to analysis the data and thanks to the vignette, I've managed it! (i think).
My current problem is that when I'm looking at the logfold changes, they are all very very small, like less than 0.5 both sides although the adjusted p value suggests that they are significant. The only thing I can think of is that it might be due to variation between the samples which i believe can be identified from the plotPCA function, although I'm not entirely clear how to interpret it. I've attached the output.
I'm at a bit of a loss on what to do next, is there a way to lower the stringency of the analysis? or should i just look at the 0.5 fold changes?
Any help would be greatly appreciated.
I'm very new to the world of RNA-seq so forgive me for my lack of knowledge.
I got some RNA-seq data from my control cells and treated cells, with 3 replicates of each. The company that did the sequencing provided a bunch of BAM files from aligning the data to the mouse genome with TopHat.
It was suggested to me that I should use DEseq to analysis the data and thanks to the vignette, I've managed it! (i think).
My current problem is that when I'm looking at the logfold changes, they are all very very small, like less than 0.5 both sides although the adjusted p value suggests that they are significant. The only thing I can think of is that it might be due to variation between the samples which i believe can be identified from the plotPCA function, although I'm not entirely clear how to interpret it. I've attached the output.
I'm at a bit of a loss on what to do next, is there a way to lower the stringency of the analysis? or should i just look at the 0.5 fold changes?
Any help would be greatly appreciated.