Hi,

I have a problem with GSVA. I tried to run Msigdb (KEGG gene set) in the GSVA using microarray data. This code worked several days ago but today when I ran it again, there was an error showed below:

 #head(exp,5)
              sample1       samples2   sample3  sample4    sample5      sample6
A1CF     2.589551   2.656472   2.524491   2.748733   2.423472   2.618552
A2M     10.299896   9.196994   8.912481   9.664004   9.301919   9.829284
A2ML1    2.870450   3.084727   3.044007   3.166133   3.211959   3.292066
A4GALT   4.173940   5.132295   4.348393   4.229899   4.569535   4.087214


library(GSEABase)
library(GSVA)

msigdb_GMTs <- "msigdb_v7.2_GMTs"
msigdb <- "c2.cp.kegg.v7.2.symbols.gmt"

geneset <- getGmt(file.path(msigdb_GMTs, msigdb))  

es.max <- gsva(exp, geneset, 
               mx.diff=FALSE, verbose=FALSE, 
               parallel.sz=1)

# Error in .mapGeneSetsToFeatures(mapped.gset.idx.list, rownames(expr)) : 
#  No identifiers in the gene sets could be matched to the identifiers in the expression data. 

sessionInfo( )
R version 4.0.3 (2020-10-10)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Big Sur 10.16

Matrix products: default
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/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] stats4    parallel  stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] forcats_0.5.1        stringr_1.4.0        dplyr_1.0.4         
 [4] purrr_0.3.4          readr_1.4.0          tidyr_1.1.2         
 [7] tibble_3.0.6         ggplot2_3.3.3        tidyverse_1.3.0     
[10] devtools_2.3.2       usethis_2.0.0        GSVA_1.39.16        
[13] GSEABase_1.52.1      graph_1.68.0         annotate_1.68.0     
[16] XML_3.99-0.5         AnnotationDbi_1.52.0 IRanges_2.24.1      
[19] S4Vectors_0.28.1     Biobase_2.50.0       BiocGenerics_0.36.0 

loaded via a namespace (and not attached):
 [1] bitops_1.0-6                matrixStats_0.58.0         
 [3] fs_1.5.0                    lubridate_1.7.9.2          
 [5] bit64_4.0.5                 httr_1.4.2                 
 [7] rprojroot_2.0.2             GenomeInfoDb_1.26.2        
 [9] tools_4.0.3                 backports_1.2.1            
[11] R6_2.5.0                    DBI_1.1.1                  
[13] colorspace_2.0-0            withr_2.4.1                
[15] tidyselect_1.1.0            prettyunits_1.1.1          
[17] processx_3.4.5              bit_4.0.4                  
[19] curl_4.3                    compiler_4.0.3             
[21] rvest_0.3.6                 cli_2.3.0                  
[23] xml2_1.3.2                  desc_1.2.0                 
[25] DelayedArray_0.16.1         scales_1.1.1               
[27] callr_3.5.1                 XVector_0.30.0             
[29] pkgconfig_2.0.3             sessioninfo_1.1.1          
[31] MatrixGenerics_1.2.1        dbplyr_2.1.0               
[33] fastmap_1.1.0               readxl_1.3.1               
[35] rlang_0.4.10                rstudioapi_0.13            
[37] RSQLite_2.2.3               generics_0.1.0             
[39] jsonlite_1.7.2              BiocParallel_1.24.1        
[41] RCurl_1.98-1.2              magrittr_2.0.1             
[43] GenomeInfoDbData_1.2.4      Matrix_1.3-2               
[45] Rcpp_1.0.6                  munsell_0.5.0              
[47] lifecycle_0.2.0             stringi_1.5.3              
[49] SummarizedExperiment_1.20.0 zlibbioc_1.36.0            
[51] pkgbuild_1.2.0              grid_4.0.3                 
[53] blob_1.2.1                  crayon_1.4.1               
[55] lattice_0.20-41             haven_2.3.1                
[57] hms_1.0.0                   ps_1.5.0                   
[59] pillar_1.4.7                GenomicRanges_1.42.0       
[61] pkgload_1.1.0               reprex_1.0.0               
[63] glue_1.4.2                  remotes_2.2.0              
[65] BiocManager_1.30.10         modelr_0.1.8               
[67] vctrs_0.3.6                 cellranger_1.1.0           
[69] testthat_3.0.1              gtable_0.3.0               
[71] assertthat_0.2.1            cachem_1.0.3               
[73] xtable_1.8-4                broom_0.7.4                
[75] memoise_2.0.0               ellipsis_0.3.1             
>

I would appreciate it if you could help me to fix this robust problem! Thank you.

Siyuan

hi,

thanks for reporting this problem, is has been fixed in the release version of GSVA 1.38.2 and in devel 1.39.17. By the way, your session information shows that you are currently using the development version of GSVA. Beware that, in general, the development version of Bioconductor packages may not work as expected because developers work on that version adding new features or refactoring code, which may lead to unexpected behavior of the package. So, unless the end user wants to beta-test new features, he/she should be using the release version.

a comment on your code, when you use the function getGmt() to read a GMT file of gene sets defined using gene symbols, I'd set the argument geneIdType=SymbolIdentifier() so that the resulting GeneSetCollection object has the additional bit of metadata that tells the type of identifier being used:

geneset <- getGmt(file.path(msigdb_GMTs, msigdb), , geneIdType=SymbolIdentifier())

this becomes useful if you need to map those identifiers to another type of identifier.

cheers,

robert.