I am trying to run the recount2 getting started guide (or a slight adaptation of it to use JHU's SciServer resource) on R 3.4.1 and Bioconductor 3.6.  I get the following output and error:

converting counts to integer mode
estimating size factors
estimating dispersions
gene-wise dispersion estimates: 14 workers
mean-dispersion relationship
final dispersion estimates, fitting model and testing: 14 workers
Error in vector(mode(x), length(NArows)): vector: cannot make a vector of mode 'S4'.
Traceback:

1. DESeq(dds, test = "LRT", reduced = ~gene_target, fitType = "local", 
 .     parallel = T)
2. DESeqParallel(object, test = test, fitType = fitType, betaPrior = betaPrior, 
 .     full = full, reduced = reduced, quiet = quiet, modelMatrix = modelMatrix, 
 .     modelMatrixType = modelMatrixType, BPPARAM = BPPARAM)
3. buildDataFrameWithNARows(mcols(objectNZ), mcols(object)$allZero)
4. DataFrame(lapply(resultsList, function(x) vector(mode(x), length(NArows))))
5. lapply(resultsList, function(x) vector(mode(x), length(NArows)))
6. lapply(resultsList, function(x) vector(mode(x), length(NArows)))
7. lapply(as.list(X), FUN = FUN, ...)
8. lapply(as.list(X), FUN = FUN, ...)
9. FUN(X[[i]], ...)

A more complete listing of the Jupyter notebook session that led to the error is here: http://www.cs.jhu.edu/~langmea/resources/recount_quickstart_error.html.

Any thoughts are much appreciated.

Best, Ben

Session info:

R version 3.4.1 (2017-06-30)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Scientific Linux 7.5 (Nitrogen)

Matrix products: default
BLAS: /home/idies/miniconda3/lib/R/lib/libRblas.so
LAPACK: /home/idies/miniconda3/lib/R/lib/libRlapack.so

locale:
[1] C

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

other attached packages:
 [1] DESeq2_1.18.1              SciServer_2.0.0           
 [3] recount_1.4.6              SummarizedExperiment_1.8.1
 [5] DelayedArray_0.4.1         matrixStats_0.53.1        
 [7] Biobase_2.38.0             GenomicRanges_1.30.3      
 [9] GenomeInfoDb_1.14.0        IRanges_2.12.0            
[11] S4Vectors_0.16.0           BiocGenerics_0.24.0       

loaded via a namespace (and not attached):
  [1] colorspace_1.3-2         IRdisplay_0.4.4          qvalue_2.10.0           
  [4] htmlTable_1.9            XVector_0.18.0           base64enc_0.1-3         
  [7] bit64_0.9-5              AnnotationDbi_1.40.0     xml2_1.2.0              
 [10] codetools_0.2-15         splines_3.4.1            geneplotter_1.56.0      
 [13] knitr_1.20               IRkernel_0.8.12          Formula_1.2-1           
 [16] jsonlite_1.5             Rsamtools_1.30.0         annotate_1.56.0         
 [19] cluster_2.0.6            rentrez_1.1.0            readr_1.1.1             
 [22] compiler_3.4.1           httr_1.3.1               backports_1.1.2         
 [25] assertthat_0.2.0         Matrix_1.2-14            lazyeval_0.2.1          
 [28] limma_3.34.9             acepack_1.4.1            htmltools_0.3.6         
 [31] prettyunits_1.0.2        tools_3.4.1              bindrcpp_0.2.2          
 [34] gtable_0.2.0             glue_1.3.0               GenomeInfoDbData_1.0.0  
 [37] reshape2_1.4.3           dplyr_0.7.6              doRNG_1.6.6             
 [40] Rcpp_0.12.17             bumphunter_1.20.0        Biostrings_2.46.0       
 [43] rtracklayer_1.38.3       iterators_1.0.10         stringr_1.3.1           
 [46] rngtools_1.3.1           XML_3.98-1.11            zlibbioc_1.24.0         
 [49] scales_0.5.0             BSgenome_1.46.0          VariantAnnotation_1.24.1
 [52] hms_0.3                  GEOquery_2.46.15         derfinderHelper_1.12.0  
 [55] RColorBrewer_1.1-2       curl_3.2                 memoise_1.1.0           
 [58] gridExtra_2.3            ggplot2_3.0.0            downloader_0.4          
 [61] pkgmaker_0.22            biomaRt_2.34.2           rpart_4.1-13            
 [64] latticeExtra_0.6-28      stringi_1.2.3            RSQLite_2.0             
 [67] genefilter_1.60.0        foreach_1.4.4            RMySQL_0.10.13          
 [70] checkmate_1.8.5          GenomicFeatures_1.28.5   BiocParallel_1.12.0     
 [73] repr_0.15.0              rlang_0.2.1              pkgconfig_2.0.1         
 [76] GenomicFiles_1.14.0      bitops_1.0-6             evaluate_0.10.1         
 [79] lattice_0.20-34          purrr_0.2.4              bindr_0.1.1             
 [82] GenomicAlignments_1.14.1 htmlwidgets_1.0          bit_1.1-12              
 [85] tidyselect_0.2.4         plyr_1.8.4               magrittr_1.5            
 [88] R6_2.2.2                 Hmisc_4.0-3              pbdZMQ_0.3-2            
 [91] DBI_1.0.0                pillar_1.2.2             foreign_0.8-67          
 [94] survival_2.42-6          RCurl_1.95-4.11          nnet_7.3-12             
 [97] tibble_1.4.2             crayon_1.3.4             derfinder_1.12.0        
[100] uuid_0.1-2               progress_1.2.0           locfit_1.5-9.1          
[103] grid_3.4.1               data.table_1.11.4        blob_1.1.1              
[106] digest_0.6.15            xtable_1.8-2             tidyr_0.8.1             
[109] munsell_0.5.0            registry_0.5

It looks like the bug is from some code I have where I build out the rowData / mcols after dealing with rows that have all zeros for all samples (that’s this buildDataFrameWithNARows). It’s having trouble with one of the columns in mcols(dds) which is not a simple mode (numeric, logical, character) but instead S4. So it’s a bug on my end (in version 1.18).

One quick fix would be to filter out genes with all zeros so DESeq2 doesn’t have to manually deal with this S4 column (I think this will work, maybe not), or  to see which column of mcols(dds) is a complex object and if it’s not necessary to remove it or convert it to something simpler. E.g.:

lapply(mcols(dds), class)
mcols(dds)$symbol <- unlist(lapply(mcols(dds)$symbol, paste, collapse=","))

Note: this appears to not be a bug in the current release branch (or if that is not the case, let me know and I'll address it).