CoembedData - Error: None of the provided refdata elements are valid.

[kylepalos]

Hi,

Thanks for maintaining a really nice R package. I am trying to use the CoembedData function as follows:

obj.coembed <- CoembedData(
  RNA,
  ATAC, 
  gene.activities, 
  weight.reduction = "umap", 
  verbose = T
)

with the following Seurat objects:

RNA
An object of class Seurat 
29875 features across 4224 samples within 2 assays 
Active assay: integrated (2000 features, 2000 variable features)
 2 layers present: data, scale.data
 1 other assay present: RNA
 2 dimensional reductions calculated: pca, umap


ATAC
An object of class Seurat 
149666 features across 5309 samples within 2 assays 
Active assay: peaks (110042 features, 110041 variable features)
 2 layers present: counts, data
 1 other assay present: RNA
 2 dimensional reductions calculated: lsi, umap

but I get the following error messages:

Performing data integration using Seurat...
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix
  |====================================================================================================| 100%
Running CCA
Merging objects
Finding neighborhoods
Finding anchors
	Found 10350 anchors
Filtering anchors
	Retained 2825 anchors
Warning: Please provide a matrix that has the same number of columns as the number of reference cells used in anchor finding.
Number of columns in provided matrix : 2976
Number of columns required           : 4224
Skipping element 1.
Error: None of the provided refdata elements are valid.
In addition: Warning messages:
1: In LayerData.Assay5(object = assays[[i]], layer = lyr, fast = TRUE) :
  multiple layers are identified by counts.1 counts.2
 only the first layer is used
2: In LayerData.Assay5(object = object[[assay]], layer = layer, ...) :
  multiple layers are identified by data.1 data.2
 only the first layer is used

I think this error likely stems from some missing metadata processing step shown in the pre-processing script. However, I am unable to follow along with the data processing script because I am processing my ATAC data with Signac, not ArchR (I am working with plant datasets and have been unsuccessful generating the input genome objects required by ArchR). Do you think this is the most likely cause of this error - or am I missing some other crucial component. Thank you very much for your help, please let me know if you need additional info, I'll include my sessionInfo below.

R version 4.3.1 (2023-06-16)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               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    LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: America/New_York
tzcode source: system (glibc)

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

other attached packages:
 [1] scMEGA_1.0.2            rtracklayer_1.60.1      hdf5r_1.3.8             GenomicFeatures_1.52.2  AnnotationDbi_1.62.2   
 [6] Biobase_2.60.0          GenomicRanges_1.52.1    GenomeInfoDb_1.36.4     IRanges_2.34.1          S4Vectors_0.38.2       
[11] BiocGenerics_0.46.0     patchwork_1.1.3         readr_2.1.4             readxl_1.4.3            tidyr_1.3.0            
[16] ggplot2_3.4.4           dplyr_1.1.3             magrittr_2.0.3          Signac_1.11.0           Seurat_4.9.9.9081      
[21] SeuratObject_4.9.9.9093 sp_2.1-1               

loaded via a namespace (and not attached):
  [1] destiny_3.14.0              matrixStats_1.0.0           spatstat.sparse_3.0-2       bitops_1.0-7               
  [5] httr_1.4.7                  RColorBrewer_1.1-3          doParallel_1.0.17           tools_4.3.1                
  [9] sctransform_0.4.1           utf8_1.2.3                  R6_2.5.1                    lazyeval_0.2.2             
 [13] uwot_0.1.16                 withr_2.5.1                 prettyunits_1.2.0           gridExtra_2.3              
 [17] progressr_0.14.0            factoextra_1.0.7            cli_3.6.1                   spatstat.explore_3.2-3     
 [21] fastDummies_1.7.3           labeling_0.4.3              robustbase_0.99-0           spatstat.data_3.0-1        
 [25] proxy_0.4-27                ggridges_0.5.4              pbapply_1.7-2               Rsamtools_2.16.0           
 [29] R.utils_2.12.2              parallelly_1.36.0           TTR_0.24.3                  rstudioapi_0.15.0          
 [33] RSQLite_2.3.1               generics_0.1.3              BiocIO_1.10.0               ica_1.0-3                  
 [37] spatstat.random_3.1-6       vroom_1.6.4                 car_3.1-2                   Matrix_1.6-1.1             
 [41] fansi_1.0.5                 abind_1.4-5                 R.methodsS3_1.8.2           lifecycle_1.0.3            
 [45] scatterplot3d_0.3-44        yaml_2.3.7                  carData_3.0-5               SummarizedExperiment_1.30.2
 [49] BiocFileCache_2.8.0         Rtsne_0.16                  grid_4.3.1                  blob_1.2.4                 
 [53] promises_1.2.1              crayon_1.5.2                miniUI_0.1.1.1              lattice_0.20-41            
 [57] cowplot_1.1.1               KEGGREST_1.40.1             pillar_1.9.0                boot_1.3-28                
 [61] rjson_0.2.21                future.apply_1.11.0         codetools_0.2-18            fastmatch_1.1-4            
 [65] leiden_0.4.3                glue_1.6.2                  pcaMethods_1.92.0           data.table_1.14.8          
 [69] remotes_2.4.2.1             vcd_1.4-11                  vctrs_0.6.4                 png_0.1-8                  
 [73] spam_2.9-1                  cellranger_1.1.0            gtable_0.3.4                cachem_1.0.8               
 [77] S4Arrays_1.0.6              mime_0.12                   tidygraph_1.2.3             RcppEigen_0.3.3.9.3        
 [81] survival_3.5-5              SingleCellExperiment_1.22.0 RcppRoll_0.3.0              pheatmap_1.0.12            
 [85] iterators_1.0.14            ellipsis_0.3.2              fitdistrplus_1.1-11         ROCR_1.0-11                
 [89] nlme_3.1-162                xts_0.13.1                  bit64_4.0.5                 progress_1.2.2             
 [93] filelock_1.0.2              RcppAnnoy_0.0.21            rprojroot_2.0.3             irlba_2.3.5.1              
 [97] KernSmooth_2.23-20          colorspace_2.1-0            DBI_1.1.3                   nnet_7.3-18                
[101] smoother_1.1                tidyselect_1.2.0            processx_3.8.2              bit_4.0.5                  
[105] compiler_4.3.1              curl_5.1.0                  xml2_1.3.5                  desc_1.4.2                 
[109] DelayedArray_0.26.7         plotly_4.10.2               scales_1.2.1                hexbin_1.28.3              
[113] DEoptimR_1.1-3              lmtest_0.9-40               callr_3.7.3                 rappdirs_0.3.3             
[117] stringr_1.5.0               digest_0.6.33               goftest_1.2-3               spatstat.utils_3.0-3       
[121] XVector_0.40.0              htmltools_0.5.6.1           pkgconfig_2.0.3             MatrixGenerics_1.12.3      
[125] dbplyr_2.3.4                fastmap_1.1.1               ggthemes_4.2.4              rlang_1.1.1                
[129] htmlwidgets_1.6.2           shiny_1.7.5.1               farver_2.1.1                zoo_1.8-12                 
[133] jsonlite_1.8.7              BiocParallel_1.34.2         R.oo_1.25.0                 RCurl_1.98-1.12            
[137] GenomeInfoDbData_1.2.10     dotCall64_1.1-0             munsell_0.5.0               Rcpp_1.0.11                
[141] viridis_0.6.4               reticulate_1.34.0           stringi_1.7.12              ggraph_2.1.0               
[145] zlibbioc_1.46.0             MASS_7.3-58.3               plyr_1.8.9                  pkgbuild_1.4.2             
[149] parallel_4.3.1              listenv_0.9.0               ggrepel_0.9.4               deldir_1.0-9               
[153] graphlayouts_1.0.1          Biostrings_2.68.1           splines_4.3.1               tensor_1.5                 
[157] hms_1.1.3                   ps_1.7.5                    ranger_0.15.1               igraph_1.5.1               
[161] spatstat.geom_3.2-7         RcppHNSW_0.5.0              reshape2_1.4.4              biomaRt_2.56.1             
[165] XML_3.99-0.14               laeken_0.5.2                tweenr_2.0.2                tzdb_0.4.0                 
[169] foreach_1.5.2               httpuv_1.6.11               VIM_6.2.2                   RANN_2.6.1                 
[173] purrr_1.0.2                 polyclip_1.10-6             future_1.33.0               scattermore_1.2            
[177] ggforce_0.4.1               xtable_1.8-4                restfulr_0.0.15             e1071_1.7-13               
[181] RSpectra_0.16-1             later_1.3.1                 viridisLite_0.4.2           class_7.3-21               
[185] tibble_3.2.1                memoise_2.0.1               GenomicAlignments_1.36.0    cluster_2.1.4              
[189] ggplot.multistats_1.0.0     globals_0.16.2