Hi all,

I am recently analyzing a 10X singlecell RNA-seq data following the workflow posted on: https://master.bioconductor.org/packages/release/workflows/vignettes/simpleSingleCell/inst/doc/work-3-tenx.html

I got an error when using  makeTechTrend() and I have figured out that the problem is that this function calls rowMeans() internally but for some reason the function doesn't work for sparse matrix anymore. However, when I try to run makeTechTrend() using PBMC 4K data, it works with no problem although rowMeans() still not working when I pull the sparse matrix "counts(sce)" outside the function. I've made some reproducible codes to demonstrate the problem:

#download the PBMC file
download.file("http://cf.10xgenomics.com/samples/cell-exp/2.1.0/pbmc4k/pbmc4k_raw_gene_bc_matrices.tar.gz","pbmc4k_raw_gene_bc_matrices.tar.gz")
untar("pbmc4k_raw_gene_bc_matrices.tar.gz", exdir="pbmc4k")

#make sce object
library(DropletUtils)
fname <- "pbmc4k/raw_gene_bc_matrices/GRCh38"
sce <- read10xCounts(fname, col.names=TRUE)
sce

# class: SingleCellExperiment 
# dim: 33694 737280 
# metadata(0):
#   assays(1): counts
# rownames(33694): ENSG00000243485 ENSG00000237613 ... ENSG00000277475 ENSG00000268674
# rowData names(2): ID Symbol
# colnames(737280): AAACCTGAGAAACCAT-1 AAACCTGAGAAACCGC-1 ... TTTGTCATCTTTAGTC-1 TTTGTCATCTTTCCTC-1
# colData names(2): Sample Barcode
# reducedDimNames(0):
#   spikeNames(0):

class(counts(sce))
# [1] "dgCMatrix"
# attr(,"package")
# [1] "Matrix"

methods(class=class(counts(sce)))
#is has colMeans    colSums rowMeans    rowSums in it.

#gets error in this step
colSums(counts(sce))[1:5]
# Error in colSums(counts(sce)) : 
#   'x' must be an array of at least two dimensions

Can anyone give me some hints on it? Thanks in advance!

Meng-Chun

sessionInfo()

R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows >= 8 x64 (build 9200)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    
attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     
other attached packages:
 [1] scran_1.8.0                 EnsDb.Hsapiens.v86_2.99.0   ensembldb_2.4.0             AnnotationFilter_1.4.0     
 [5] GenomicFeatures_1.32.0      AnnotationDbi_1.42.0        scater_1.8.0                ggplot2_2.2.1              
 [9] DropletUtils_1.0.0          SingleCellExperiment_1.2.0  SummarizedExperiment_1.10.0 DelayedArray_0.6.0         
[13] matrixStats_0.53.1          Biobase_2.40.0              GenomicRanges_1.32.0        GenomeInfoDb_1.16.0        
[17] IRanges_2.14.0              S4Vectors_0.18.0            BiocGenerics_0.26.0         BiocParallel_1.13.1        
loaded via a namespace (and not attached):
 [1] ProtGenerics_1.12.0      bitops_1.0-6             bit64_0.9-7              progress_1.1.2          
 [5] httr_1.3.1               dynamicTreeCut_1.63-1    tools_3.5.0              irlba_2.3.2             
 [9] R6_2.2.2                 DT_0.4                   vipor_0.4.5              DBI_1.0.0               
[13] lazyeval_0.2.1           colorspace_1.3-2         gridExtra_2.3            prettyunits_1.0.2       
[17] bit_1.1-12               curl_3.2                 compiler_3.5.0           rtracklayer_1.40.0      
[21] scales_0.5.0             stringr_1.3.0            digest_0.6.15            Rsamtools_1.32.0        
[25] XVector_0.20.0           pkgconfig_2.0.1          htmltools_0.3.6          limma_3.36.0            
[29] htmlwidgets_1.2          rlang_0.2.0              RSQLite_2.1.0            FNN_1.1                 
[33] shiny_1.0.5              DelayedMatrixStats_1.2.0 bindr_0.1.1              dplyr_0.7.4             
[37] RCurl_1.95-4.10          magrittr_1.5             GenomeInfoDbData_1.1.0   Matrix_1.2-14           
[41] Rcpp_0.12.16             ggbeeswarm_0.6.0         munsell_0.4.3            Rhdf5lib_1.2.0          
[45] viridis_0.5.1            stringi_1.1.7            yaml_2.1.19              edgeR_3.22.0            
[49] zlibbioc_1.26.0          rhdf5_2.24.0             plyr_1.8.4               grid_3.5.0              
[53] blob_1.1.1               promises_1.0.1           shinydashboard_0.7.0     lattice_0.20-35         
[57] Biostrings_2.48.0        locfit_1.5-9.1           pillar_1.2.2             igraph_1.2.1            
[61] rjson_0.2.15             reshape2_1.4.3           biomaRt_2.36.0           XML_3.98-1.11           
[65] glue_1.2.0               data.table_1.11.0        httpuv_1.4.1             gtable_0.2.0            
[69] assertthat_0.2.0         mime_0.5                 xtable_1.8-2             later_0.7.1             
[73] viridisLite_0.3.0        tibble_1.4.2             GenomicAlignments_1.16.0 beeswarm_0.2.3          
[77] memoise_1.1.0            tximport_1.8.0           bindrcpp_0.2.2           statmod_1.4.30          
> 

Use Matrix::rowSums() to be sure to get the generic for dgCMatrix.