I have run a DEXSeq analysis according to the vignette and run into the following error after executing testForDEU():

Error in (function (classes, fdef, mtable)  :
  unable to find an inherited method for function ‘mcols’ for signature ‘"matrix"’

The traceback of this error was the following:

> traceback()
4: stop(gettextf("unable to find an inherited method for function %s for signature %s",
       sQuote(fdef@generic), sQuote(cnames)), domain = NA)
3: (function (classes, fdef, mtable)
   {
       methods <- .findInheritedMethods(classes, fdef, mtable)
       if (length(methods) == 1L)
           return(methods[[1L]])
       else if (length(methods) == 0L) {
           cnames <- paste0("\"", vapply(classes, as.character,
               ""), "\"", collapse = ", ")
           stop(gettextf("unable to find an inherited method for function %s for signature %s",
               sQuote(fdef@generic), sQuote(cnames)), domain = NA)
       }
       else stop("Internal error in finding inherited methods; didn't return a unique method",
           domain = NA)
   })(list("matrix"), function (x, use.names = FALSE, ...)
   standardGeneric("mcols"), <environment>)
2: mcols(mergeObject)
1: testForDEU(DEX.data)

However, mcols(DEX.data) before running testForDEU seems to be fine and is of class DataFrame (as expected). The error occurs despite the fact that there are no warnings / errors in any of the previous steps. I have updated all my packages (did not help) and am running the latest R version (see below). Does anyone have any suggestions as to how to solve this?

My code was as follows:

######## Preprocessing
## Set variables
gtf.file <- "~/Peeper/Resources/Tophat_GTF/Hs66.gtf"
flattened.gff.file <- "~/Peeper/Resources/Tophat_GTF/Hs66.DEXSeq.chr.gff"

## Execute python scripts
pythonScriptsDir <- system.file("python_scripts", package = "DEXSeq")
system(paste("python", file.path(pythonScriptsDir, "dexseq_prepare_annotation.py"), gtf.file, flattened.gff.file))

## Loop over bam files to execute DEXseq python script
bam.files <- list.files(pattern = ".bam$", recursive = TRUE)

## If files are sorted by sort -k 1,1 already:
library(BiocParallel)
bp.param <- SnowParam(workers = 8)
DexseqCount <- function(bam.files, pythonScriptsDir, flattened.gff.file) {
    system(paste("samtools view", bam.files, "| python", file.path(pythonScriptsDir, "dexseq_count.py"), "--paired=no --stranded=no", flattened.gff.file, "-", gsub("accepted_hits.bam$", "dexseq_counts.txt", bam.files)))
}
bplapply(bam.files, DexseqCount, pythonScriptsDir, flattened.gff.file, BPPARAM = bp.param)

## Create sample.annotation
count.files <- list.files(pattern = "dexseq_counts.txt$", recursive = TRUE)
sample.annotation <- data.frame(row.names = dirname(bam.files), responder = c("responder", "non-responder")[c(2, 1, 1, 1, 2, 1, 2, 2, 2, 1, 2, 2, 1, 1, 2, 2, 2, 1, 2, 1, 1, 2, 1, 2, 1, 2, 1, 1, 2, 2, 1, 2, 2)])

###### Relevant DEXSeq analysis
## Load library & create DEXSeqDataSet
suppressPackageStartupMessages(library("DEXSeq"))
DEX.data <- DEXSeqDataSetFromHTSeq(count.files, sampleData = sample.annotation, design = ~ responder + exon, flattenedfile = flattened.gff.file)

## Estimate size factors & dispersions
DEX.data <- estimateSizeFactors(DEX.data)
DEX.data <- estimateDispersions(DEX.data, BPPARAM = bp.param)
save(DEX.data, file = "./DEX_data_estimate_dispersion.Rdata", compress = "xz")
plotDispEsts(DEX.data)

## Perform actual DE analysis
DEX.data <- testForDEU(DEX.data, BPPARAM = bp.param)

My sessionInfo was:

> sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 14.04.1 LTS

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    parallel  stats     graphics  grDevices utils     datasets
[8] methods   base     

other attached packages:
 [1] DEXSeq_1.16.7              DESeq2_1.10.1             
 [3] RcppArmadillo_0.6.500.4.0  Rcpp_0.12.3               
 [5] SummarizedExperiment_1.0.2 GenomicRanges_1.22.3      
 [7] GenomeInfoDb_1.6.3         IRanges_2.4.6             
 [9] S4Vectors_0.8.7            Biobase_2.30.0            
[11] BiocGenerics_0.16.1        BiocParallel_1.4.3        

loaded via a namespace (and not attached):
 [1] RColorBrewer_1.1-2   futile.logger_1.4.1  plyr_1.8.3          
 [4] XVector_0.10.0       bitops_1.0-6         tools_3.2.3         
 [7] futile.options_1.0.0 zlibbioc_1.16.0      biomaRt_2.26.1      
[10] statmod_1.4.23       rpart_4.1-10         RSQLite_1.0.0       
[13] annotate_1.48.0      gtable_0.1.2         lattice_0.20-33     
[16] DBI_0.3.1            gridExtra_2.0.0      stringr_1.0.0       
[19] hwriter_1.3.2        genefilter_1.52.1    cluster_2.0.3       
[22] Biostrings_2.38.3    locfit_1.5-9.1       grid_3.2.3          
[25] nnet_7.3-11          snow_0.4-1           AnnotationDbi_1.32.3
[28] XML_3.98-1.3         survival_2.38-3      foreign_0.8-66      
[31] latticeExtra_0.6-26  Formula_1.2-1        magrittr_1.5        
[34] geneplotter_1.48.0   ggplot2_2.0.0        lambda.r_1.1.7      
[37] Rsamtools_1.22.0     Hmisc_3.17-1         scales_0.3.0        
[40] splines_3.2.3        colorspace_1.2-6     xtable_1.8-0        
[43] stringi_1.0-1        acepack_1.3-3.3      RCurl_1.95-4.7      
[46] munsell_0.4.2       

Thanks for your detailed report and sorry for the uninformative error message. Probably the problem is coming from the design that you are specifying, which does not uses the interaction between the exon and any of your variables. If you want to see the differences on exon usage between responders, you should specify the formula:

 ~ sample + exon + responder:exon

More examples are specified in the vignette.

Alejandro