I'm trying to import salmon quant tables with tximeta.

I did the transcript quantification with salmon as below:

salmon quant -i ../gencode.v32_salmon0.14.1 -l A \
         -1 ${samp}_1.fastq.gz \
         -2 ${samp}_2.fastq.gz \
         --numGibbsSamples 20 \
         -p 8 --gcBias --validateMappings -o ../salmon_quant/${samp}_quant

When I'm trying to import it as a summarized experiment in R I get the following error.

> se <- tximeta(coldata)
importing quantifications
reading in files with read_tsv
1 2 3 4 5 6 
couldn't find matching transcriptome, returning un-ranged SummarizedExperiment
Error in stopifnot(all(sapply(infReps, ncol) == nreps)) : 
  (list) object cannot be coerced to type 'integer'

I don't know what's going and appreciate the help. Thanks

> sessionInfo()
R version 3.6.1 (2019-07-05)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14.5

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/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] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] tximeta_1.2.2        TSAtools_0.0.0.9000  GOfuncR_1.4.0       
 [4] vioplot_0.3.4        zoo_1.8-7            sm_2.2-5.6          
 [7] rtracklayer_1.44.4   GenomicRanges_1.36.1 GenomeInfoDb_1.20.0 
[10] IRanges_2.18.3       S4Vectors_0.22.1     BiocGenerics_0.30.0 

loaded via a namespace (and not attached):
 [1] Biobase_2.44.0              httr_1.4.1                 
 [3] jsonlite_1.6                bit64_0.9-7                
 [5] gtools_3.8.1                assertthat_0.2.1           
 [7] BiocFileCache_1.8.0         blob_1.2.0                 
 [9] GenomeInfoDbData_1.2.1      Rsamtools_2.0.3            
[11] progress_1.2.2              pillar_1.4.3               
[13] RSQLite_2.2.0               backports_1.1.5            
[15] lattice_0.20-38             glue_1.3.1                 
[17] digest_0.6.23               XVector_0.24.0             
[19] Matrix_1.2-18               XML_3.98-1.20              
[21] pkgconfig_2.0.3             biomaRt_2.40.5             
[23] zlibbioc_1.30.0             purrr_0.3.3                
[25] BiocParallel_1.18.1         tibble_2.1.3               
[27] AnnotationFilter_1.8.0      SummarizedExperiment_1.14.1
[29] GenomicFeatures_1.36.4      lazyeval_0.2.2             
[31] magrittr_1.5                crayon_1.3.4               
[33] memoise_1.1.0               tools_3.6.1                
[35] prettyunits_1.1.0           hms_0.5.3                  
[37] matrixStats_0.55.0          stringr_1.4.0              
[39] DelayedArray_0.10.0         AnnotationDbi_1.46.1       
[41] ensembldb_2.8.1             Biostrings_2.52.0          
[43] compiler_3.6.1              mapplots_1.5.1             
[45] rlang_0.4.2                 grid_3.6.1                 
[47] RCurl_1.95-4.12             tximport_1.12.3            
[49] rstudioapi_0.10             rappdirs_0.3.1             
[51] bitops_1.0-6                tcltk_3.6.1                
[53] DBI_1.1.0                   curl_4.3                   
[55] R6_2.4.1                    GenomicAlignments_1.20.1   
[57] knitr_1.26                  dplyr_0.8.3                
[59] bit_1.1-15                  zeallot_0.1.0              
[61] ProtGenerics_1.16.0         readr_1.3.1                
[63] stringi_1.4.5               Rcpp_1.0.3                 
[65] vctrs_0.2.1                 dbplyr_1.4.2               
[67] tidyselect_0.2.5            xfun_0.12

Thanks for the report.

It looks like I should add some more checks here, but what I'd guess is happening is that not all of the samples have inferential replicates, and this is breaking code that assumes they all do have inferential replicates.

Can you check by looking into the meta_info.json file for each sample to see that they all have inferential replicates?