Error: invalid subscript in addGeneExpressionMatrix()

[YesseniaCQ]

Hello,
Thank you for creating this tool, it is extremely useful. And, congratulations on your recent publication.
I keep getting this error when I try to add gene expression from a paired multi-omics project. I am not sure what is causing it, or how to fix it. Error: invalid subscript happened when I used the addGeneExpressionMatrix function. Can you help?
My log file is attached.

My code:

seRNA=as.SingleCellExperiment(rna_proj)
seRNA2=as(seRNA, "RangedSummarizedExperiment")

atac_proj <- addGeneExpressionMatrix(
  input = atac_proj,
  seRNA = seRNA2,
)

The error I get:

ArchR logging to : ArchRLogs/ArchR-addGeneExpressionMatrix-220b5bf3dac0-Date-2024-02-20_Time-22-02-21.09538.log
If there is an issue, please report to github with logFile!
2024-02-20 22:02:28.794287 : Overlap w/ scATAC = 1
2024-02-20 22:02:28.863532 : Overlap Per Sample w/ scATAC : testis01=399,testis02=68,testis03=182
Error: invalid subscript

the atac_proj

samples(3): testis01 testis03 testis02
sampleColData names(1): ArrowFiles
cellColData names(21): Sample TSSEnrichment ... ReadsInPeaks FRIP
numberOfCells(1): 649
medianTSS(1): 12.383
medianFrags(1): 6622

the seRNA2

class: RangedSummarizedExperiment 
dim: 21369 649 
metadata(0):
assays(2): counts logcounts
rownames(21369): OR4F5 FAM87B ... F8A2 TMLHE
rowData names(0):
colnames(649): testis01#AAAGCACCAGTTGCGT-1 testis01#AAATGCCTCCTCATGC-1
  ... testis03#TTTGCGACAGCAAATA-1 testis03#TTTGTGAAGTGCTGTG-1
colData names(18): orig.ident nCount_RNA ... cell.type ident

ArchR-addGeneExpressionMatrix-220b5bf3dac0-Date-2024-02-20_Time-22-02-21.09538.log

And this is my sessionInfo

R version 4.3.2 (2023-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /public/home/ycq2022/anaconda3/envs/seurat4.3/lib/libopenblasp-r0.3.25.so;  LAPACK version 3.11.0

Random number generation:
 RNG:     L'Ecuyer-CMRG 
 Normal:  Inversion 
 Sample:  Rejection 
 
locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Asia/Shanghai
tzcode source: system (glibc)

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

other attached packages:
 [1] clusterProfiler_4.10.0            mclust_6.0.1                     
 [3] presto_1.0.0                      SingleCellExperiment_1.24.0      
 [5] nabor_0.5.0                       BSgenome.Hsapiens.UCSC.hg38_1.4.5
 [7] BSgenome_1.70.2                   rtracklayer_1.62.0               
 [9] BiocIO_1.12.0                     Biostrings_2.70.2                
[11] XVector_0.42.0                    ComplexHeatmap_2.18.0            
[13] tidyr_1.3.1                       dplyr_1.1.4                      
[15] SeuratObject_4.1.3                Seurat_4.3.0.1                   
[17] rhdf5_2.44.0                      SummarizedExperiment_1.32.0      
[19] Biobase_2.62.0                    MatrixGenerics_1.14.0            
[21] Matrix_1.5-4.1                    GenomicRanges_1.54.1             
[23] GenomeInfoDb_1.38.6               IRanges_2.36.0                   
[25] S4Vectors_0.40.2                  BiocGenerics_0.48.1              
[27] matrixStats_1.2.0                 data.table_1.15.0                
[29] stringr_1.5.1                     plyr_1.8.9                       
[31] magrittr_2.0.3                    ggplot2_3.4.4                    
[33] gtable_0.3.4                      gtools_3.9.5                     
[35] gridExtra_2.3                     ArchR_1.0.2                      
[37] harmony_1.2.0                     Rcpp_1.0.12                      

loaded via a namespace (and not attached):
  [1] fs_1.6.3                 spatstat.sparse_3.0-3    bitops_1.0-7            
  [4] enrichplot_1.22.0        HDO.db_0.99.1            httr_1.4.7              
  [7] RColorBrewer_1.1-3       doParallel_1.0.17        tools_4.3.2             
 [10] sctransform_0.4.1        utf8_1.2.4               R6_2.5.1                
 [13] lazyeval_0.2.2           uwot_0.1.16              rhdf5filters_1.12.1     
 [16] GetoptLong_1.0.5         withr_3.0.0              sp_2.1-3                
 [19] progressr_0.14.0         cli_3.6.2                spatstat.explore_3.2-6  
 [22] scatterpie_0.2.1         spatstat.data_3.0-4      ggridges_0.5.6          
 [25] pbapply_1.7-2            Rsamtools_2.18.0         yulab.utils_0.1.4       
 [28] gson_0.1.0               DOSE_3.28.1              parallelly_1.37.0       
 [31] RSQLite_2.3.5            generics_0.1.3           gridGraphics_0.5-1      
 [34] shape_1.4.6              ica_1.0-3                spatstat.random_3.2-2   
 [37] GO.db_3.18.0             fansi_1.0.6              abind_1.4-5             
 [40] lifecycle_1.0.4          yaml_2.3.8               qvalue_2.34.0           
 [43] SparseArray_1.2.2        Rtsne_0.17               blob_1.2.4              
 [46] promises_1.2.1           crayon_1.5.2             miniUI_0.1.1.1          
 [49] lattice_0.22-5           cowplot_1.1.3            KEGGREST_1.42.0         
 [52] pillar_1.9.0             fgsea_1.28.0             rjson_0.2.21            
 [55] future.apply_1.11.1      codetools_0.2-19         fastmatch_1.1-4         
 [58] leiden_0.4.3.1           glue_1.7.0               ggfun_0.1.4             
 [61] vctrs_0.6.5              png_0.1-8                treeio_1.26.0           
 [64] cachem_1.0.8             S4Arrays_1.2.0           mime_0.12               
 [67] tidygraph_1.3.0          survival_3.5-8           iterators_1.0.14        
 [70] ellipsis_0.3.2           fitdistrplus_1.1-11      ROCR_1.0-11             
 [73] nlme_3.1-164             ggtree_3.10.0            bit64_4.0.5             
 [76] RcppAnnoy_0.0.22         irlba_2.3.5.1            KernSmooth_2.23-22      
 [79] colorspace_2.1-0         DBI_1.2.2                tidyselect_1.2.0        
 [82] bit_4.0.5                compiler_4.3.2           DelayedArray_0.28.0     
 [85] plotly_4.10.4            shadowtext_0.1.3         scales_1.3.0            
 [88] lmtest_0.9-40            digest_0.6.34            goftest_1.2-3           
 [91] spatstat.utils_3.0-4     RhpcBLASctl_0.23-42      htmltools_0.5.7         
 [94] pkgconfig_2.0.3          fastmap_1.1.1            rlang_1.1.3             
 [97] GlobalOptions_0.1.2      htmlwidgets_1.6.4        shiny_1.8.0             
[100] farver_2.1.1             zoo_1.8-12               jsonlite_1.8.8          
[103] BiocParallel_1.36.0      GOSemSim_2.28.0          RCurl_1.98-1.14         
[106] GenomeInfoDbData_1.2.11  ggplotify_0.1.2          patchwork_1.2.0         
[109] Rhdf5lib_1.22.0          munsell_0.5.0            ape_5.7-1               
[112] viridis_0.6.5            reticulate_1.35.0        stringi_1.8.3           
[115] ggraph_2.1.0             zlibbioc_1.48.0          MASS_7.3-60.0.1         
[118] org.Hs.eg.db_3.18.0      listenv_0.9.1            ggrepel_0.9.5           
[121] deldir_2.0-2             graphlayouts_1.1.0       splines_4.3.2           
[124] tensor_1.5               circlize_0.4.15          igraph_2.0.2            
[127] spatstat.geom_3.2-8      reshape2_1.4.4           XML_3.99-0.16.1         
[130] BiocManager_1.30.22      foreach_1.5.2            tweenr_2.0.2            
[133] httpuv_1.6.14            RANN_2.6.1               purrr_1.0.2             
[136] polyclip_1.10-6          future_1.33.1            clue_0.3-65             
[139] scattermore_1.2          ggforce_0.4.2            xtable_1.8-4            
[142] restfulr_0.0.15          tidytree_0.4.6           later_1.3.2             
[145] viridisLite_0.4.2        tibble_3.2.1             aplot_0.2.2             
[148] memoise_2.0.1            AnnotationDbi_1.64.1     GenomicAlignments_1.38.2
[151] cluster_2.1.6            globals_0.16.2

[rcorces]

Hi @YesseniaCQ! Thanks for using ArchR! Please make sure that your post belongs in the Issues section. Only bugs and error reports belong in the Issues section. Usage questions and feature requests should be posted in the Discussions section, not in Issues.

It is worth noting that there are very few actual bugs in ArchR. If you are getting an error, it is probably something specific to your dataset, usage, or computational environment, all of which are extremely challenging to troubleshoot. As such, we require reproducible examples (preferably using the tutorial dataset) from users who want assistance. If you cannot reproduce your error, we will not be able to help.
Before going through the work of making a reproducible example, search the previous Issues, Discussions, function definitions, or the ArchR manual and you will likely find the answers you are looking for.
If your post does not contain a reproducible example, it is unlikely to receive a response.

__In addition to a reproducible example, you must do the following things before we help you, unless your original post already contained this information:
1. If you've encountered an error, have you already searched previous Issues to make sure that this hasn't already been solved?
2. Did you post your log file? If not, add it now.
3. Remove any screenshots that contain text and instead copy and paste the text using markdown's codeblock syntax (three consecutive backticks). You can do this by editing your original post.

[rferina]

I had this error when trying to run addGeneExpressionMatrix using seRNA = as.SingleCellExperiment(seurat_obj). In case this helps anyone, turns out my converted object was missing rowRanges. I fixed this by running import10xFeatureMatrix -- see multiome_tutorial and subsetting to ensure I only kept cells in common between my archR project and seRNA