Hi,
I have been working with edgeR to find differentially expressed genes for RNA-seq data. I have been working with a data set with 3 treatment groups and a total of 10 samples per treatment group. The samples were sequenced as single-end, stranded reads. I first analyzed this dataset with the edgeR v2.6 and was getting 100-300 ( FDR<0.05, tagwise dispersion with prior.n=20) differentially expressed genes for each pairwise comparison. I upgraded to version 3.2.4 this weekend and reanalyzed the same dataset. I now get <100 genes as being differentially expressed (FDR<0.05, tagwise dispersion with prior.df=20) across comparisons. Does anyone know why there would be such a big difference in # of genes being called DEGS? The smaller gene list is complete subset of the larger gene list so I am assuming that some upgrades caused edgeR to be more conservative.
Thanks,
I have been working with edgeR to find differentially expressed genes for RNA-seq data. I have been working with a data set with 3 treatment groups and a total of 10 samples per treatment group. The samples were sequenced as single-end, stranded reads. I first analyzed this dataset with the edgeR v2.6 and was getting 100-300 ( FDR<0.05, tagwise dispersion with prior.n=20) differentially expressed genes for each pairwise comparison. I upgraded to version 3.2.4 this weekend and reanalyzed the same dataset. I now get <100 genes as being differentially expressed (FDR<0.05, tagwise dispersion with prior.df=20) across comparisons. Does anyone know why there would be such a big difference in # of genes being called DEGS? The smaller gene list is complete subset of the larger gene list so I am assuming that some upgrades caused edgeR to be more conservative.
Thanks,
