Error with FindDoublets::paramSweep

[kunleng]

Running FindDoublets::paramSweep results in the following error:

Running SCTransform on assay: RNA
Running SCTransform on layer: counts
Error in [.default(cell.attr, colnames(x = layer.data), , drop = FALSE) :
incorrect number of dimensions

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 22.04.4 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0

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/Los_Angeles
tzcode source: system (glibc)

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

other attached packages:
[1] sctransform_0.4.1 fields_15.2 viridisLite_0.4.2
[4] spam_2.10-0 DoubletFinder_2.0.4 mousecortexref.SeuratData_1.0.0
[7] SeuratData_0.2.2.9001 Azimuth_0.5.0 shinyBS_0.61.1
[10] Seurat_5.0.3 SeuratObject_5.0.1 sp_2.1-3
[13] lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1
[16] dplyr_1.1.4 purrr_1.0.2 readr_2.1.5
[19] tidyr_1.3.1 tibble_3.2.1 ggplot2_3.5.0
[22] tidyverse_2.0.0

loaded via a namespace (and not attached):
[1] fs_1.6.3 ProtGenerics_1.34.0 matrixStats_1.2.0
[4] spatstat.sparse_3.0-3 bitops_1.0-7 DirichletMultinomial_1.44.0
[7] TFBSTools_1.40.0 httr_1.4.7 RColorBrewer_1.1-3
[10] tools_4.3.2 utf8_1.2.4 R6_2.5.1
[13] DT_0.32 lazyeval_0.2.2 uwot_0.1.16
[16] rhdf5filters_1.14.1 withr_3.0.0 prettyunits_1.2.0
[19] gridExtra_2.3 progressr_0.14.0 cli_3.6.2
[22] Biobase_2.62.0 spatstat.explore_3.2-7 fastDummies_1.7.3
[25] EnsDb.Hsapiens.v86_2.99.0 shinyjs_2.1.0 labeling_0.4.3
[28] sass_0.4.9 spatstat.data_3.0-4 ggridges_0.5.6
[31] pbapply_1.7-2 Rsamtools_2.18.0 R.utils_2.12.3
[34] parallelly_1.37.1 maps_3.4.2 BSgenome_1.70.2
[37] rstudioapi_0.16.0 RSQLite_2.3.5 generics_0.1.3
[40] BiocIO_1.12.0 gtools_3.9.5 ica_1.0-3
[43] spatstat.random_3.2-3 googlesheets4_1.1.1 GO.db_3.18.0
[46] Matrix_1.6-3 fansi_1.0.6 S4Vectors_0.40.2
[49] abind_1.4-5 R.methodsS3_1.8.2 lifecycle_1.0.4
[52] yaml_2.3.8 SummarizedExperiment_1.32.0 glmGamPoi_1.14.3
[55] rhdf5_2.46.1 SparseArray_1.2.4 BiocFileCache_2.10.2
[58] Rtsne_0.17 grid_4.3.2 blob_1.2.4
[61] promises_1.2.1 shinydashboard_0.7.2 crayon_1.5.2
[64] miniUI_0.1.1.1 lattice_0.22-5 cowplot_1.1.3
[67] annotate_1.80.0 GenomicFeatures_1.54.4 KEGGREST_1.42.0
[70] pillar_1.9.0 knitr_1.45 GenomicRanges_1.54.1
[73] rjson_0.2.21 future.apply_1.11.1 codetools_0.2-19
[76] fastmatch_1.1-4 leiden_0.4.3.1 glue_1.7.0
[79] data.table_1.15.2 remotes_2.5.0 vctrs_0.6.5
[82] png_0.1-8 cellranger_1.1.0 poweRlaw_0.80.0
[85] gtable_0.3.4 cachem_1.0.8 xfun_0.43
[88] Signac_1.12.0 S4Arrays_1.2.1 mime_0.12
[91] pracma_2.4.4 survival_3.5-7 gargle_1.5.2
[94] RcppRoll_0.3.0 fitdistrplus_1.1-11 ROCR_1.0-11
[97] nlme_3.1-163 bit64_4.0.5 progress_1.2.3
[100] filelock_1.0.3 RcppAnnoy_0.0.22 GenomeInfoDb_1.38.8
[103] bslib_0.6.2 irlba_2.3.5.1 KernSmooth_2.23-22
[106] SeuratDisk_0.0.0.9021 colorspace_2.1-0 seqLogo_1.68.0
[109] BiocGenerics_0.48.1 DBI_1.2.2 tidyselect_1.2.1
[112] bit_4.0.5 compiler_4.3.2 curl_5.2.1
[115] hdf5r_1.3.10 xml2_1.3.6 DelayedArray_0.28.0
[118] plotly_4.10.4 rtracklayer_1.62.0 scales_1.3.0
[121] caTools_1.18.2 lmtest_0.9-40 rappdirs_0.3.3
[124] digest_0.6.35 goftest_1.2-3 presto_1.0.0
[127] spatstat.utils_3.0-4 rmarkdown_2.26 XVector_0.42.0
[130] htmltools_0.5.8 pkgconfig_2.0.3 sparseMatrixStats_1.14.0
[133] MatrixGenerics_1.14.0 dbplyr_2.5.0 fastmap_1.1.1
[136] ensembldb_2.26.0 rlang_1.1.3 htmlwidgets_1.6.4
[139] DelayedMatrixStats_1.24.0 shiny_1.8.1 farver_2.1.1
[142] jquerylib_0.1.4 zoo_1.8-12 jsonlite_1.8.8
[145] BiocParallel_1.36.0 R.oo_1.26.0 RCurl_1.98-1.14
[148] magrittr_2.0.3 GenomeInfoDbData_1.2.11 dotCall64_1.1-1
[151] patchwork_1.2.0 Rhdf5lib_1.24.2 munsell_0.5.0
[154] Rcpp_1.0.12 reticulate_1.35.0 stringi_1.8.3
[157] zlibbioc_1.48.2 MASS_7.3-60 plyr_1.8.9
[160] listenv_0.9.1 ggrepel_0.9.5 CNEr_1.38.0
[163] deldir_2.0-4 Biostrings_2.70.3 splines_4.3.2
[166] tensor_1.5 hms_1.1.3 BSgenome.Hsapiens.UCSC.hg38_1.4.5
[169] igraph_2.0.3 spatstat.geom_3.2-9 RcppHNSW_0.6.0
[172] reshape2_1.4.4 biomaRt_2.58.2 stats4_4.3.2
[175] TFMPvalue_0.0.9 XML_3.99-0.16.1 evaluate_0.23
[178] BiocManager_1.30.22 JASPAR2020_0.99.10 tzdb_0.4.0
[181] httpuv_1.6.15 RANN_2.6.1 polyclip_1.10-6
[184] future_1.33.2 scattermore_1.2 xtable_1.8-4
[187] restfulr_0.0.15 AnnotationFilter_1.26.0 RSpectra_0.16-1
[190] later_1.3.2 googledrive_2.1.1 memoise_2.0.1
[193] AnnotationDbi_1.64.1 GenomicAlignments_1.38.2 IRanges_2.36.0
[196] cluster_2.1.6 timechange_0.3.0 globals_0.16.3

pbmc_small <- pbmc_small %>%
SCTransform() %>%
RunPCA() %>%
FindNeighbors(dims = 1:30) %>%
FindClusters() %>%
RunUMAP(dims = 1:30)
sweep.res.list <- paramSweep(pbmc_small, PCs = 1:30, sct = T)