Hi,

I have a question regarding edgeR, more specifically the glmQLFTest function. I cannot run glmQLFTest, whereas I have no troubles running  glmLRT on the same dataset. Here's my code:

#Creation of the DGElist object
sceset_filt <- readRDS("../intermediate/sceset_filt.rds")  #this is a SingleCellExperiment object
group <- colData(sceset_filt)$cell_identity
counts <- counts(sceset_filt)
y <- DGEList(counts = counts, group = group)
y$genes <- rowData(sceset_filt)$symbol

#Filter to remove low counts
keep <- rowSums(edgeR::cpm(y) > 0.5) >= 2
y$counts <- y$counts[keep,]
y$genes <- y$genes[keep]

y <- calcNormFactors(y)
design <- model.matrix(~ 0 + group)
colnames(design) <- levels(group)
y <- estimateDisp(y, design = design)
con1 <- makeContrasts("Brain_double_positive - Ubrain_double_positive", levels=design)

#when trying to perform quasi-likelihood F-test
fit <- glmQLFit(y, design = design)
res <- glmQLFTest(fit, coef = ncol(fit$design), contrast = con1)
Error in object[[a]][i, , drop = FALSE] : incorrect number of dimensions

#when performing LRT test
fit2 <- glmFit(y,design = design)
lrt <- glmLRT(fit2, coef = ncol(fit2$design), contrast = con1)
topTags(lrt)
Coefficient:  1*Brain_double_positive -1*Ubrain_double_positive
                          ID     logFC    logCPM        LR       PValue
ENSMUSG00000020591     Ntsr2 -5.067292 10.211872 170.58070 5.525055e-39
ENSMUSG00000031760       Mt3 -4.659873 10.607507 164.66813 1.080953e-37
ENSMUSG00000017390     Aldoc -3.601116 12.490920 124.93592 5.256501e-29
...

My "fit" seems like a valid object of class "DGEGLM". In RStudio, the traceback shows:

Error in object[[a]][i, , drop = FALSE] : incorrect number of dimensions

4. subsetListOfArrays(object, i, j, IJ = IJ, IX = IX, I = I, JX = JX)

3. [.DGEGLM`(glmfit, i, )

2. glmfit[i, ]

1. glmQLFTest(fit, contrast = con1)

Can someone help me understand what the problem is?

Thanks!

> sessionInfo()

R version 3.5.0 (2018-04-23)

Platform: x86_64-apple-darwin15.6.0 (64-bit)

Running under: macOS High Sierra 10.13.5

Matrix products: default

BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib

LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib

locale:

[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

attached base packages:

[1] stats4    parallel  stats     graphics  grDevices utils     datasets

[8] methods   base     

other attached packages:

[1] scater_1.8.0                SingleCellExperiment_1.2.0

[3] SummarizedExperiment_1.10.1 DelayedArray_0.6.1         

[5] BiocParallel_1.14.1         matrixStats_0.53.1         

[7] GenomicRanges_1.32.3        GenomeInfoDb_1.16.0        

[9] IRanges_2.14.10             S4Vectors_0.18.3           

[11] ggplot2_2.2.1               Biobase_2.40.0             

[13] BiocGenerics_0.26.0         edgeR_3.22.3               

[15] limma_3.36.2                BiocInstaller_1.30.0       

loaded via a namespace (and not attached):

[1] Rcpp_0.12.17             locfit_1.5-9.1           lattice_0.20-35         

[4] assertthat_0.2.0         digest_0.6.15            mime_0.5                

[7] R6_2.2.2                 plyr_1.8.4               pillar_1.2.3            

[10] zlibbioc_1.26.0          rlang_0.2.1              lazyeval_0.2.1          

[13] data.table_1.11.4        Matrix_1.2-14            splines_3.5.0           

[16] stringr_1.3.1            RCurl_1.95-4.10          munsell_0.5.0           

[19] shiny_1.1.0              compiler_3.5.0           httpuv_1.4.4.1          

[22] vipor_0.4.5              pkgconfig_2.0.1          ggbeeswarm_0.6.0        

[25] htmltools_0.3.6          tximport_1.8.0           tidyselect_0.2.4        

[28] tibble_1.4.2             gridExtra_2.3            GenomeInfoDbData_1.1.0

[31] viridisLite_0.3.0        dplyr_0.7.5              later_0.7.3             

[34] bitops_1.0-6             grid_3.5.0               xtable_1.8-2            

[37] gtable_0.2.0             magrittr_1.5             scales_0.5.0            

[40] stringi_1.2.3            XVector_0.20.0           reshape2_1.4.3          

[43] viridis_0.5.1            promises_1.0.1           bindrcpp_0.2.2          

[46] DelayedMatrixStats_1.2.0 rjson_0.2.20             Rhdf5lib_1.2.1          

[49] tools_3.5.0              glue_1.2.0               beeswarm_0.2.3          

[52] purrr_0.2.5              colorspace_1.3-2         rhdf5_2.24.0            

[55] shinydashboard_0.7.0     bindr_0.1.1             

I'm willing to bet that the problem is caused by the fact that y$genes should be a data.frame, not a character vector. Tryre-running the code after replacing your y$genes <- assignment with:

y$genes <- data.frame(Symbol=rowData(sceset_filt)$symbol)