I have recently obtained very promising results using the diffSplice and diffsSpliceDGE from limma/edgeR, respectively. I was surprised to find that neither method has a cited reference despite being included in both the main limma paper and both edgeR and limma user guides. DEXSeq in comparison has a separate reference in addition to DESeq/DESeq2.
This meant that I had to piece together what the method actually does from the help files from diffSplice/topSplice and diffSpliceDGE/topSliceDGE.
As far as I can tell, diffSplice works directly from the model fitted in a normal limma/edgeR analysis, unlike DEXSeq which fits a separate model including the exons, although it still uses the same dispersion estimation from DESeq2.
As I understand, the F-statistics test tests whether any exon logFC is different from any other, yielding a single gene-level p-value. The exon-level test tests whether each exon has a logFC different from the average across genes. These exon-level p-values are then corrected using the Simes method, before using the lowest p-value of among exons to represent the gene.
I am unfamiliar with the Simes method for correcting p-values. Conceptually, the approach seems similar to DEXSeq's approach with perGeneQvalue, where p-values are defined first at the exon level, and then aggregated at the gene level (Asking whether at least one exon-level p-value is significant in the gene). Intuitively, how is aggregating exon-level p-values using the Simes method different from using DEXSeq perGeneQvalue? Does it possibly relate to the comment that "The exon-level tests are not recommended for formal error rate control." from the help files?
Any insight or pointers to resources are much appreciated.
Thank for your reply. I haven't done any systematic investigation, but it does indeed on the face of it seems that the F-test tends to mainly find differential splicing in genes with many exons, whereas the Simes correction seem to be more stable across different number of exons.
With regards the the exon-level test, I'm actually not using RNA-Seq data, but rather look at expression from different promoters of the same gene. In that case, there is no uncertainty in quantification of counts, since each transcript uniquely uses a single promoter. Would that mean that the error rate is controlled in this case?
No, it has nothing to do with uncertainty of quantification. Regardless of the nature of your data, is it not statistically correct to apply FDR control at a lower level (promotors or exons) when the ultimate aim is to interpret results at a higher level (genes). Simply looking for genes in which any exon has a low p-value will tend to select genes with a large number of exons, just by chance. Simes method has the effect of making the minimum p-value for each gene uniformly distributed, regardless of the number of exons in that gene. See my reply to your other comment.