Hello,

I am attempting to construct a dds object out some GTEx data obtained through Recount. When creating the dds object, I am met with an error that I believe is due to integers in the data that are too large for R. I have been unable to find a workaround for this thus far. The following output displays the command that generated the error, the error itself, and my session info.

Thanks, Zach

> dds_merge <- DESeqDataSet(rse_merge, design = ~1) %>% estimateSizeFactors()
converting counts to integer mode
Error in validObject(.Object) : 
  invalid class “DESeqDataSet” object: NA values are not allowed in the count matrix
In addition: Warning message:
In mde(x) : NAs introduced by coercion to integer range
> sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 16299)

Matrix products: default

Random number generation:
 RNG:     Mersenne-Twister 
 Normal:  Inversion 
 Sample:  Rounding 

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] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] forcats_0.4.0               stringr_1.4.0               dplyr_0.8.0.1              
 [4] purrr_0.3.2                 readr_1.3.1                 tidyr_0.8.3                
 [7] tibble_2.1.1                ggplot2_3.1.1               tidyverse_1.2.1            
[10] DESeq2_1.24.0               SummarizedExperiment_1.14.0 DelayedArray_0.10.0        
[13] BiocParallel_1.17.18        matrixStats_0.54.0          Biobase_2.44.0             
[16] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0         IRanges_2.18.0             
[19] S4Vectors_0.22.0            BiocGenerics_0.30.0        

loaded via a namespace (and not attached):
 [1] nlme_3.1-139           bitops_1.0-6           lubridate_1.7.4        bit64_0.9-7           
 [5] RColorBrewer_1.1-2     httr_1.4.0             tools_3.6.0            backports_1.1.4       
 [9] R6_2.4.0               rpart_4.1-15           Hmisc_4.2-0            DBI_1.0.0             
[13] lazyeval_0.2.2         colorspace_1.4-1       nnet_7.3-12            withr_2.1.2           
[17] tidyselect_0.2.5       gridExtra_2.3          bit_1.1-14             compiler_3.6.0        
[21] cli_1.1.0              rvest_0.3.3            htmlTable_1.13.1       xml2_1.2.0            
[25] scales_1.0.0           checkmate_1.9.3        genefilter_1.66.0      digest_0.6.18         
[29] foreign_0.8-71         XVector_0.24.0         base64enc_0.1-3        pkgconfig_2.0.2       
[33] htmltools_0.3.6        htmlwidgets_1.3        rlang_0.3.4            readxl_1.3.1          
[37] rstudioapi_0.10        RSQLite_2.1.1          generics_0.0.2         jsonlite_1.6          
[41] acepack_1.4.1          RCurl_1.95-4.12        magrittr_1.5           GenomeInfoDbData_1.2.1
[45] Formula_1.2-3          Matrix_1.2-17          Rcpp_1.0.1             munsell_0.5.0         
[49] stringi_1.4.3          zlibbioc_1.30.0        plyr_1.8.4             grid_3.6.0            
[53] blob_1.1.1             crayon_1.3.4           lattice_0.20-38        haven_2.1.0           
[57] splines_3.6.0          annotate_1.62.0        hms_0.4.2              locfit_1.5-9.1        
[61] knitr_1.22             pillar_1.4.0           geneplotter_1.62.0     XML_3.98-1.19         
[65] glue_1.3.1             latticeExtra_0.6-28    data.table_1.12.2      modelr_0.1.4          
[69] cellranger_1.1.0       gtable_0.3.0           assertthat_0.2.1       xfun_0.7              
[73] xtable_1.8-4           broom_0.5.2            survival_2.44-1.1      AnnotationDbi_1.46.0  
[77] memoise_1.1.0          cluster_2.0.8         

Are you sure that you don't have an NA in the counts?

You can use anyis.na(...)) on the counts assay for example to test this yourself.