Hello everyone,

I was exploring my dataset with a three-way interaction design (~TimePoint * Drug * Cell Line) and decided to check if, when comparing two levels of one of the variables, if the shrunken log2FC using apeglm would vary when releveled. For example, I want to check if the shrunken log2FC between TimePoint 1 and 10 would vary if we do TimePoint_10_vs_1 or TimePoint_1_vs_10. I checked and indeed are some relevant differences (I did subtraction of absolute values):

> summary(Dif)
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-2.703706 -0.000064  0.000000  0.000917  0.000062  1.914137

Now, considering the complexity of my dataset, I decided to check if this would happen in a simpler dataset - so I created an example DESeq Datataset:

set.seed(123)

dds <- makeExampleDESeqDataSet(n = 10000,m=20)
dds

dds_example<-DESeq(dds)


res_BvA_shrink<-lfcShrink(dds_example,coef = "condition_B_vs_A")

dds_example$condition<-relevel(dds_example$condition,ref = "B")
dds_example<-nbinomWaldTest(dds_example)


res_AvB_shrink<-lfcShrink(dds_example,coef = "condition_A_vs_B")

Looking at the difference of absolute values between the two:

> summary(abs(res_BvA_shrink$log2FoldChange)-abs(res_AvB_shrink$log2FoldChange))
    Min.  1st Qu.   Median     Mean  3rd Qu.     Max.     NA's 
-3.20841 -0.00003  0.00000  0.00012  0.00002  3.08496        8

Is this expected? Can someone please help me to understand the logic?

Thanks in advance

Please see: apeglm -- influence of reference level on logFC and svalues

I asked basically the same before. In short, reference level might have an influence in some corner cases, so decide for a reference level and stick with it.