I am having problems running scran::modelGeneVar, the error seems to come from BiocParallel not collecting the data correctly from the workers: wrong args for environment subassignment.

But I am not sure as I think I ran this code without problems previously. I am sorry I couldn't reduce the example to something reproducible, but I hope that the information provided is enough to redirect me . In previous process on the same R session I saw a spike on computer usage to more than 50 cores when multi_core only asks for 10 (maybe it is related).

Code and related information:

> gene_var_filt2 <- scran::modelGeneVar(gf_rescales, 
+                                       block = gf_rescales$type,
+                                       BPPARAM = multi_core,
+                                       assay.type = "corrected")
  |                                                               |   0%
Error in reducer$value.cache[[as.character(idx)]] <- values : 
  wrong args for environment subassignment
> traceback()
20: .reducer_add(reducer, njob, value)
19: .reducer_add(reducer, njob, value)
18: .collect_result(manager, reducer, progress, BPPARAM)
17: .bploop_impl(ITER = ITER, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM, 
        BPOPTIONS = BPOPTIONS, BPREDO = BPREDO, reducer = reducer, 
        progress.length = length(redo_index))
16: bploop.lapply(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, 
        ...)
15: bploop(manager, BPPARAM = BPPARAM, BPOPTIONS = BPOPTIONS, ...)
14: .bpinit(manager = manager, X = X, FUN = FUN, ARGS = ARGS, BPPARAM = BPPARAM, 
        BPOPTIONS = BPOPTIONS, BPREDO = BPREDO)
13: BiocParallel::bplapply(X, FUN, ..., BPPARAM = BPPARAM)
12: BiocParallel::bplapply(X, FUN, ..., BPPARAM = BPPARAM)
11: S4Arrays:::bplapply2(seq_along(grid), FUN_WRAPPER, grid, verbose, 
        FUN, ..., BPPARAM = BPPARAM)
10: gridApply(grid, FUN_WRAPPER, FUN, x, as.sparse, verbose, verbose_read_block, 
        ..., BPPARAM = BPPARAM, verbose = FALSE)
9: blockApply(x, FUN = .sparse_helper, beachmat_internal_FUN = FUN, 
       ..., grid = grid, as.sparse = NA, BPPARAM = BPPARAM)
8: .blockApply2(x, FUN = FUN, ..., grid = grid, coerce.sparse = coerce.sparse, 
       BPPARAM = BPPARAM, beachmat_by_row = TRUE)
7: rowBlockApply(x, FUN = block.FUN, block = as.integer(block) - 
       1L, nblocks = nlevels(block), ..., BPPARAM = BPPARAM) at utils_variance.R#31
6: .compute_mean_var(x, block = block, design = design, subset.row = s, 
       block.FUN = compute_blocked_stats_none, residual.FUN = compute_residual_stats_none, 
       BPPARAM = BPPARAM) at modelGeneVar.R#149
5: FUN(subset.row) at modelGeneVar.R#154
4: .model_gene_var(x = assay(x, i = assay.type), ...) at modelGeneVar.R#186
3: .local(x, ...)
2: scran::modelGeneVar(gf_rescales, block = gf_rescales$type, BPPARAM = multi_core, 
       assay.type = "corrected") at modelGeneVar.R#176
1: scran::modelGeneVar(gf_rescales, block = gf_rescales$type, BPPARAM = multi_core, 
       assay.type = "corrected")

> sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.5 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0

locale:
 [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=es_ES.UTF-8        LC_COLLATE=en_GB.UTF-8    
 [5] LC_MONETARY=es_ES.UTF-8    LC_MESSAGES=en_GB.UTF-8   
 [7] LC_PAPER=es_ES.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=es_ES.UTF-8 LC_IDENTIFICATION=C       

time zone: Europe/Madrid
tzcode source: system (glibc)

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

other attached packages:
 [1] dplyr_1.1.4                 forcats_1.0.0              
 [3] scran_1.32.0                scds_1.20.0                
 [5] scDblFinder_1.18.0          scater_1.32.0              
 [7] scuttle_1.14.0              robustbase_0.99-2          
 [9] PCAtools_2.16.0             ggrepel_0.9.5              
[11] patchwork_1.2.0             here_1.0.1                 
[13] batchelor_1.20.0            GO.db_3.19.1               
[15] ggtext_0.1.2                DropletUtils_1.24.0        
[17] SingleCellExperiment_1.26.0 SummarizedExperiment_1.34.0
[19] DelayedMatrixStats_1.26.0   DelayedArray_0.30.1        
[21] SparseArray_1.4.8           S4Arrays_1.4.1             
[23] abind_1.4-5                 Matrix_1.7-0               
[25] MatrixGenerics_1.16.0       matrixStats_1.3.0          
[27] clustree_0.5.1              ggraph_2.2.1               
[29] ggplot2_3.5.1               bluster_1.14.0             
[31] biomaRt_2.60.0              ensembldb_2.28.0           
[33] AnnotationFilter_1.28.0     GenomicFeatures_1.56.0     
[35] AnnotationDbi_1.66.0        Biobase_2.64.0             
[37] GenomicRanges_1.56.1        GenomeInfoDb_1.40.1        
[39] IRanges_2.38.1              S4Vectors_0.42.1           
[41] BiocSingular_1.20.0         BiocParallel_1.38.0        
[43] AnnotationHub_3.12.0        BiocFileCache_2.12.0       
[45] dbplyr_2.5.0                BiocGenerics_0.50.0        
[47] usethis_2.2.3               targets_1.7.1              

loaded via a namespace (and not attached):
  [1] BiocIO_1.14.0            bitops_1.0-7            
  [3] filelock_1.0.3           tibble_3.2.1            
  [5] R.oo_1.26.0              cellranger_1.1.0        
  [7] polyclip_1.10-6          pROC_1.18.5             
  [9] XML_3.99-0.17            lifecycle_1.0.4         
 [11] httr2_1.0.1              edgeR_4.2.0             
 [13] rprojroot_2.0.4          processx_3.8.4          
 [15] lattice_0.22-6           MASS_7.3-60.2           
 [17] backports_1.5.0          magrittr_2.0.3          
 [19] limma_3.60.3             yaml_2.3.10             
 [21] metapod_1.12.0           cowplot_1.1.3           
 [23] DBI_1.2.3                ResidualMatrix_1.14.1   
 [25] zlibbioc_1.50.0          purrr_1.0.2             
 [27] R.utils_2.12.3           RCurl_1.98-1.14         
 [29] tweenr_2.0.3             rappdirs_0.3.3          
 [31] GenomeInfoDbData_1.2.12  irlba_2.3.5.1           
 [33] dqrng_0.4.1              codetools_0.2-20        
 [35] xml2_1.3.6               ggforce_0.4.2           
 [37] tidyselect_1.2.1         UCSC.utils_1.0.0        
 [39] farver_2.1.2             ScaledMatrix_1.12.0     
 [41] viridis_0.6.5            GenomicAlignments_1.40.0
 [43] jsonlite_1.8.8           BiocNeighbors_1.22.0    
 [45] tidygraph_1.3.1          systemfonts_1.1.0       
 [47] tools_4.4.0              progress_1.2.3          
 [49] ragg_1.3.2               Rcpp_1.0.13             
 [51] glue_1.7.0               gridExtra_2.3           
 [53] xfun_0.46                HDF5Array_1.32.0        
 [55] withr_3.0.0              BiocManager_1.30.23     
 [57] fastmap_1.2.0            rhdf5filters_1.16.0     
 [59] fansi_1.0.6              callr_3.7.6             
 [61] digest_0.6.36            rsvd_1.0.5              
 [63] secretbase_1.0.0         R6_2.5.1                
 [65] textshaping_0.4.0        colorspace_2.1-1        
 [67] scattermore_1.2          RSQLite_2.3.7           
 [69] R.methodsS3_1.8.2        utf8_1.2.4              
 [71] tidyr_1.3.1              generics_0.1.3          
 [73] renv_1.0.7.9000          data.table_1.15.4       
 [75] rtracklayer_1.64.0       prettyunits_1.2.0       
 [77] graphlayouts_1.1.1       httr_1.4.7              
 [79] pkgconfig_2.0.3          gtable_0.3.5            
 [81] blob_1.2.4               XVector_0.44.0          
 [83] base64url_1.4            ProtGenerics_1.36.0     
 [85] scales_1.3.0             png_0.1-8               
 [87] knitr_1.48               rstudioapi_0.16.0       
 [89] reshape2_1.4.4           rjson_0.2.21            
 [91] curl_5.2.1               cachem_1.1.0            
 [93] rhdf5_2.48.0             stringr_1.5.1           
 [95] BiocVersion_3.19.1       parallel_4.4.0          
 [97] vipor_0.4.7              restfulr_0.0.15         
 [99] pillar_1.9.0             grid_4.4.0              
 [ reached getOption("max.print") -- omitted 34 entries ]

Input data:

> gf_rescales
class: SingleCellExperiment 
dim: 33047 306459 
metadata(0):
assays(1): corrected
rownames(33047): ENSG00000243485 ENSG00000238009 ...
  ENSG00000278817 ENSG00000277196
rowData names(0):
colnames: NULL
colData names(10): Sample Barcode ... date_infection
  post_omicron
reducedDimNames(1): PCA
mainExpName: NULL
altExpNames(0):
> multi_core
class: MulticoreParam
  bpisup: FALSE; bpnworkers: 10; bptasks: 2147483647; bpjobname: BPJOB
  bplog: FALSE; bpthreshold: INFO; bpstopOnError: TRUE
  bpRNGseed: ; bptimeout: NA; bpprogressbar: TRUE
  bpexportglobals: TRUE; bpexportvariables: FALSE; bpforceGC: FALSE
  bpfallback: TRUE
  bplogdir: NA
  bpresultdir: NA
  cluster type: FORK

The gf_rescales$type column is present.