Error in .make_numeric_version(x, strict, .standard_regexps()$valid_numeric_version) : invalid non-character version specification 'x' (type: double)

[ayusyarif]

Hi,

I got an error when running RunNiches() stating that

Compute edgelist based on mutual nearest neighbors. Error in .make_numeric_version(x, strict, .standard_regexps()$valid_numeric_version) : invalid non-character version specification 'x' (type: double)

does anyone know how to solve this problem? I have reinstalled the package but still same.

best regards, Ayu

[nmeskow]

Running into this issue as well. Trying to analyze spatial transcriptomics data, which is currently loaded into a Seurat object.

#Load packages
library(Seurat)
library(NICHES)

#Load in Seurat object
seu <- readRDS(file = "/Users/nmeskow/ST/seu.rds")
# Split object into conditions, to calculate NICHES objects separately
data.list <- SplitObject(seu, split.by="group_id")

# Run NICHES on each system and store/name the outputs
scc.list <- list()
for(i in 1:length(data.list)){
scc.list[[i]] <- RunNICHES(data.list[[i]], LR.database="fantom5", species="mouse", assay="Spatial", cell_types = "seurat_clusters", position.x = "x", position.y = "y",  min.cells.per.ident = 0, min.cells.per.gene = NULL, CellToCell = F, CellToCellSpatial = T, NeighborhoodToCell = T, CellToNeighborhood = T)
 }

Output:

Set cell types as Identity of object internally

Subsetting to populations with greater than 0 cells

10090 distinct cells from 14 celltypes to be analyzed

Limiting ground truth to genes within dataset

Mapping against 2227 ground truth signaling mechanisms
Compute edgelist based on mutual nearest neighbors.
Error in .make_numeric_version(x, strict, .standard_regexps()$valid_numeric_version) : 
  invalid non-character version specification 'x' (type: double)

Session info here:

R version 4.4.1 (2024-06-14)
Platform: aarch64-apple-darwin20
Running under: macOS Sonoma 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/4.4-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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

other attached packages:
 [1] devtools_2.4.5     usethis_3.0.0      nicheDE_0.0.0.9000 cowplot_1.1.3      reshape2_1.4.4     stringr_1.5.1     
 [7] NICHES_1.1.0       scales_1.3.0       dplyr_1.1.4        ggplot2_3.5.1      Seurat_5.1.0       SeuratObject_5.0.2
[13] sp_2.1-4          

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22       splines_4.4.1          later_1.3.2            tibble_3.2.1           polyclip_1.10-7       
  [6] fastDummies_1.7.4      lifecycle_1.0.4        rstatix_0.7.2          doParallel_1.0.17      globals_0.16.3        
 [11] processx_3.8.4         lattice_0.22-6         MASS_7.3-61            backports_1.5.0        magrittr_2.0.3        
 [16] plotly_4.10.4          rmarkdown_2.28         yaml_2.3.10            remotes_2.5.0          httpuv_1.6.15         
 [21] sctransform_0.4.1      spam_2.10-0            sessioninfo_1.2.2      pkgbuild_1.4.4         spatstat.sparse_3.1-0 
 [26] reticulate_1.39.0      pbapply_1.7-2          RColorBrewer_1.1-3     abind_1.4-8            pkgload_1.4.0         
 [31] Rtsne_0.17             purrr_1.0.2            BiocGenerics_0.50.0    circlize_0.4.16        IRanges_2.38.1        
 [36] S4Vectors_0.42.1       ggrepel_0.9.6          irlba_2.3.5.1          listenv_0.9.1          spatstat.utils_3.1-0  
 [41] goftest_1.2-3          RSpectra_0.16-2        spatstat.random_3.3-2  fitdistrplus_1.2-1     parallelly_1.38.0     
 [46] leiden_0.4.3.1         codetools_0.2-20       tidyselect_1.2.1       shape_1.4.6.1          farver_2.1.2          
 [51] matrixStats_1.4.1      stats4_4.4.1           spatstat.explore_3.3-2 jsonlite_1.8.8         GetoptLong_1.0.5      
 [56] ellipsis_0.3.2         progressr_0.14.0       ggridges_0.5.6         survival_3.7-0         iterators_1.0.14      
 [61] foreach_1.5.2          tools_4.4.1            ica_1.0-3              Rcpp_1.0.13            glue_1.7.0            
 [66] gridExtra_2.3          xfun_0.47              mgcv_1.9-1             withr_3.0.1            fastmap_1.2.0         
 [71] fansi_1.0.6            callr_3.7.6            digest_0.6.37          R6_2.5.1               mime_0.12             
 [76] colorspace_2.1-1       scattermore_1.2        tensor_1.5             spatstat.data_3.1-2    utf8_1.2.4            
 [81] tidyr_1.3.1            generics_0.1.3         data.table_1.16.0      httr_1.4.7             htmlwidgets_1.6.4     
 [86] uwot_0.2.2             pkgconfig_2.0.3        gtable_0.3.5           ComplexHeatmap_2.20.0  lmtest_0.9-40         
 [91] htmltools_0.5.8.1      carData_3.0-5          profvis_0.3.8          dotCall64_1.1-1        clue_0.3-65           
 [96] png_0.1-8              spatstat.univar_3.0-1  knitr_1.48             rstudioapi_0.16.0      rjson_0.2.23          
[101] nlme_3.1-166           curl_5.2.2             zoo_1.8-12             cachem_1.1.0           GlobalOptions_0.1.2   
[106] KernSmooth_2.23-24     parallel_4.4.1         miniUI_0.1.1.1         STdeconvolve_1.8.0     desc_1.4.3            
[111] pillar_1.9.0           grid_4.4.1             vctrs_0.6.5            RANN_2.6.2             urlchecker_1.0.1      
[116] promises_1.3.0         ggpubr_0.6.0           car_3.1-2              xtable_1.8-4           cluster_2.1.6         
[121] evaluate_1.0.0         cli_3.6.3              compiler_4.4.1         rlang_1.1.4            crayon_1.5.3          
[126] future.apply_1.11.2    ggsignif_0.6.4         ps_1.8.0               plyr_1.8.9             fs_1.6.4              
[131] stringi_1.8.4          viridisLite_0.4.2      deldir_2.0-4           munsell_0.5.1          lazyeval_0.2.2        
[136] spatstat.geom_3.3-3    Matrix_1.7-0           RcppHNSW_0.6.0         patchwork_1.3.0        future_1.34.0         
[141] shiny_1.9.1            ROCR_1.0-11            igraph_2.0.3           broom_1.0.6            memoise_2.0.1 

[ayusyarif]

Hi, I think it is related to seurat compatibility. I solved this by modifying the Seurat::GetAssayData() inside the core function, in my case is RunCellToCellSpatial().

RunCellToCellSpatial <- function(sys.small,
                                 ground.truth,
                                 assay,
                                 meta.data.to.map,
                                 edgelist,
                                 output_format
){
subunit.list <- matrix(data = 1,nrow = nrow(ground.truth$source.subunits),ncol = ncol(Seurat::GetAssayData(sys.small,assay,"data")[,edgelist$from])) #initialize a mechanism x barcode matrix of all NAs
  colnames(subunit.list) <- colnames(Seurat::GetAssayData(sys.small,assay,"data")[,edgelist$from])
  rownames(subunit.list) <- rownames(ground.truth$source.subunits)
  non.na.indices <- !is.na(ground.truth$source.subunits) #Identify rows in the s-th column of the ground truth which are not NA
  subunit.list[non.na.indices,] <- as.matrix(Seurat::GetAssayData(sys.small,assay,"data")[ground.truth$source.subunits[non.na.indices,],edgelist$from])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
lig.data <- subunit.list
rm(subunit.list)

# Make receptor matrix
  subunit.list <- matrix(data = 1,nrow = nrow(ground.truth$target.subunits),ncol = ncol(Seurat::GetAssayData(sys.small,assay,"data")[,edgelist$to])) #initialize a mechanism x barcode matrix of all NAs
  colnames(subunit.list) <- colnames(Seurat::GetAssayData(sys.small,assay,"data")[,edgelist$to])
  rownames(subunit.list) <- rownames(ground.truth$target.subunits)
  non.na.indices <- !is.na(ground.truth$target.subunits) #Identify rows in the t-th column of the ground truth which are not NA
  subunit.list[non.na.indices,] <- as.matrix(Seurat::GetAssayData(sys.small,assay,"data")[ground.truth$target.subunits[non.na.indices,],edgelist$to])   #For every row in the initialized matrix corresponding to the indices of the ground.truth which are not NA, replace with the rows from the Seurat object corresponding to the genes in the ground.truth at those indices
rec.data <- subunit.list
rm(subunit.list)

# Make SCC matrix
scc <- lig.data*rec.data
rownames(scc) <- paste(rownames(lig.data),rownames(rec.data),sep = '—')
colnames(scc) <- paste(colnames(lig.data),colnames(rec.data),sep = '—')
sending.cell.idents <- as.character(Seurat::Idents(sys.small)[colnames(lig.data)])
receiving.cell.idents <- as.character(Seurat::Idents(sys.small)[colnames(rec.data)])
dim(scc)

# Use this matrix to create a Seurat object:
demo <- Seurat::CreateSeuratObject(counts = as.matrix(scc),assay = 'CellToCellSpatial')

# JC: Seurat V5 will not create data slot automatically, the following step is to manually add this slot
demo <- NormalizeData(demo,assay = "CellToCellSpatial")  # Seura Object need to be >= 5.0.1
demo@assays$CellToCellSpatial@layers$data <- demo@assays$CellToCellSpatial@layers$counts # Seura Object need to be >= 5.0.1

# Add key metadata

meta.data.to.add <- data.frame(SendingType = sending.cell.idents,
                               ReceivingType = receiving.cell.idents)

rownames(meta.data.to.add) <- colnames(scc)
meta.data.to.add$VectorType <- paste(meta.data.to.add$SendingType,
                                     meta.data.to.add$ReceivingType,
                                     sep = '—')

#Add metadata to the Seurat object
demo <- Seurat::AddMetaData(demo,metadata = meta.data.to.add)

# Gather and assemble additional metadata
if (!is.null(meta.data.to.map)){
  # Identify sending and receiving barcodes
  sending.barcodes <- colnames(lig.data)
  receiving.barcodes <- colnames(rec.data)
  # Pull and format sending and receiving metadata
  # jc: possible bug, change object to sys.small
  sending.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][sending.barcodes,])
  receiving.metadata <- as.matrix(sys.small@meta.data[,meta.data.to.map,drop=FALSE][receiving.barcodes,])
  # Make joint metadata
  datArray <- abind::abind(sending.metadata,receiving.metadata,along=3)
  joint.metadata <- as.matrix(apply(datArray,1:2,function(x)paste(x[1],"-",x[2])))
  # Define column names
  colnames(joint.metadata) <- paste(colnames(sending.metadata),'Joint',sep = '.')
  colnames(sending.metadata) <- paste(colnames(sending.metadata),'Sending',sep='.')
  colnames(receiving.metadata) <- paste(colnames(receiving.metadata),'Receiving',sep='.')
  # Compile
  meta.data.to.add.also <- cbind(sending.metadata,receiving.metadata,joint.metadata)
  rownames(meta.data.to.add.also) <- paste(sending.barcodes,receiving.barcodes,sep='—')
  # Add additional metadata
  demo <- Seurat::AddMetaData(demo,metadata = as.data.frame(meta.data.to.add.also))
}

# Set initial identity
Seurat::Idents(demo) <- demo$VectorType

# How many vectors were captured by this sampling?
message(paste("\n",length(unique(demo$VectorType)),'distinct VectorTypes were computed, out of',length(table(Seurat::Idents(sys.small)))^2,'total possible'))

return(demo)
}

Best, Ayu