I recently started using the cqn package and have found a small error in the vignette. In code chunk 17, the glm.offset is put into the d.montobject and then estimateGLMCommonDisp is run to create a new object d.mont.cqn:

###################################################
### code chunk number 17: edgeRdisp
###################################################
design <- model.matrix(~ d.mont$sample$group)
d.mont$offset <- cqn.subset$glm.offset
d.mont.cqn <- estimateGLMCommonDisp(d.mont, design = design)

All this is fine and the results are shown in chunk 19:

> ###################################################
> ### code chunk number 19: cqn.Rnw:264-265
> ###################################################
> summary(decideTestsDGE(elrt.cqn))
       d.mont$sample$groupgrp2
Down                       146
NotSig                   22971
Up                         435

However, in code chunk 20 the d.mont object is re-used, but since it still contains the glm.offset, the results are exactly the same:

> ###################################################
> ### code chunk number 20: edgeRstd
> ###################################################
> d.mont.std <- estimateGLMCommonDisp(d.mont, design = design)
> efit.std <- glmFit(d.mont.std, design = design)
> elrt.std <- glmLRT(efit.std, coef = 2)
> summary(decideTestsDGE(elrt.std))
       d.mont$sample$groupgrp2
Down                       146
NotSig                   22971
Up                         435

The quickest fix would be to just set the offsets back to NULL:

> d.mont$offset <- NULL
> d.mont.std <- estimateGLMCommonDisp(d.mont, design = design)
> efit.std <- glmFit(d.mont.std, design = design)
> elrt.std <- glmLRT(efit.std, coef = 2)
> summary(decideTestsDGE(elrt.std))
       d.mont$sample$groupgrp2
Down                       211
NotSig                   23086
Up                         255

I might also suggest updating to the edgeR-quasi method as it's the new preferred way. Here are the codes I've been using:

> ###################################################
> ### code chunk number 17: edgeRdisp
> ###################################################
> design <- model.matrix(~ d.mont$sample$group)
> d.mont$offset <- cqn.subset$glm.offset
> d.mont.cqn <- estimateDisp(d.mont, design = design) 
> 
> 
> ###################################################
> ### code chunk number 18: edgeRfit
> ###################################################
> efit.cqn <- glmQLFit(d.mont.cqn, design = design)
> eqlf.cqn <- glmQLFTest(efit.cqn, coef = 2)
> topTags(eqlf.cqn, n = 2)
Coefficient:  d.mont$sample$groupgrp2 
                length gccontent      logFC   logCPM        F       PValue         FDR
ENSG00000253701    359 0.6100279   7.504784 3.608416 55.69780 6.437842e-08 0.001516241
ENSG00000211642    365 0.5835616 -10.252487 6.362877 28.67522 1.329693e-05 0.156584594
> 
> 
> ###################################################
> ### code chunk number 19: cqn.Rnw:264-265
> ###################################################
> summary(decideTestsDGE(eqlf.cqn))
       d.mont$sample$groupgrp2
Down                         0
NotSig                   23551
Up                           1
> 
> ###################################################
> ### code chunk number 20: edgeRstd
> ###################################################
> d.mont$offset <- NULL
> d.mont.std <- estimateDisp(d.mont, design = design)
> efit.std <- glmQLFit(d.mont.std, design = design)
> eqlf.std <- glmQLFTest(efit.std, coef = 2)
> summary(decideTestsDGE(eqlf.std))
       d.mont$sample$groupgrp2
Down                        10
NotSig                   23534
Up                           8

Although with the edgeR-quasi method, very few genes reach the FDR < 0.05 threshold in this example.

Thanks for the cqn package!

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

Matrix products: default

locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252   
[3] LC_MONETARY=English_United States.1252 LC_NUMERIC=C                          
[5] LC_TIME=English_United States.1252    

attached base packages:
[1] splines   stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] edgeR_3.28.0          limma_3.42.0          scales_1.1.0          cqn_1.32.0           
[5] quantreg_5.52         SparseM_1.77          preprocessCore_1.48.0 nor1mix_1.3-0        
[9] mclust_5.4.5         

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.3         locfit_1.5-9.1     lattice_0.20-38    grid_3.6.1         R6_2.4.1          
 [6] lifecycle_0.1.0    MatrixModels_0.4-1 rlang_0.4.2        farver_2.0.1       Matrix_1.2-17     
[11] tools_3.6.1        munsell_0.5.0      compiler_3.6.1     colorspace_1.4-1