Error in colSums when running RunAzimuth()

[defneercelen]

Hello, I'm trying to use RunAzimuth but getting the error below. I saw some other posts with the same error, but none of the solutions to those helped (restarting R, updating all packages, updating all Bioconductor packages, etc.)

Here is my code and session info:

library(Seurat)
library(magrittr)
library(tibble)
library(dplyr)
library(cowplot)
library(ggplot2)
library(viridis)
library(ggsci)
library(tidyverse)
library(patchwork)
library('harmony')
library(glmGamPoi)
library('Azimuth')
library(SeuratData)
library(SeuratDisk)
library(Matrix)
library(SeuratObject)
library('TFBSTools')

run = readRDS()
azi_run <- RunAzimuth(run, reference = "pbmcref")

error:
Warning: Overwriting miscellanous data for model
Warning: Adding a dimensional reduction (refUMAP) without the associated assay being present
Warning: Adding a dimensional reduction (refUMAP) without the associated assay being present
detected inputs from HUMAN with id type Gene.name
reference rownames detected HUMAN with id type Gene.name
Error in .local(x, na.rm, dims, ...) :
object 'CRsparse_colSums' not found

sessionInfo()
R version 4.3.2 (2023-10-31)
Platform: x86_64-apple-darwin20 (64-bit)
Running under: macOS Ventura 13.6.1

Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

time zone: America/New_York
tzcode source: internal

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

other attached packages:
[1] TFBSTools_1.40.0 Matrix_1.6-4 SeuratDisk_0.0.0.9021 pbmcref.SeuratData_1.0.0 SeuratData_0.2.2.9001
[6] Azimuth_0.5.0 shinyBS_0.61.1 glmGamPoi_1.14.0 harmony_1.2.0 Rcpp_1.0.11
[11] patchwork_1.1.3 lubridate_1.9.3 forcats_1.0.0 stringr_1.5.1 purrr_1.0.2
[16] readr_2.1.4 tidyr_1.3.0 tidyverse_2.0.0 ggsci_3.0.0 viridis_0.6.4
[21] viridisLite_0.4.2 ggplot2_3.4.4 cowplot_1.1.1 dplyr_1.1.4 tibble_3.2.1
[26] magrittr_2.0.3 Seurat_5.0.1 SeuratObject_5.0.1 sp_2.1-2

loaded via a namespace (and not attached):
[1] fs_1.6.3 ProtGenerics_1.34.0 matrixStats_1.1.0
[4] spatstat.sparse_3.0-3 bitops_1.0-7 DirichletMultinomial_1.44.0
[7] httr_1.4.7 RColorBrewer_1.1-3 tools_4.3.2
[10] sctransform_0.4.1 utf8_1.2.4 R6_2.5.1
[13] DT_0.31 lazyeval_0.2.2 uwot_0.1.16
[16] rhdf5filters_1.14.1 withr_2.5.2 prettyunits_1.2.0
[19] gridExtra_2.3 progressr_0.14.0 cli_3.6.2
[22] Biobase_2.62.0 spatstat.explore_3.2-5 fastDummies_1.7.3
[25] EnsDb.Hsapiens.v86_2.99.0 shinyjs_2.1.0 spatstat.data_3.0-3
[28] ggridges_0.5.4 pbapply_1.7-2 Rsamtools_2.18.0
[31] R.utils_2.12.3 parallelly_1.36.0 BSgenome_1.70.1
[34] rstudioapi_0.15.0 RSQLite_2.3.4 generics_0.1.3
[37] BiocIO_1.12.0 gtools_3.9.5 ica_1.0-3
[40] spatstat.random_3.2-2 googlesheets4_1.1.1 GO.db_3.18.0
[43] fansi_1.0.6 S4Vectors_0.40.2 abind_1.4-5
[46] R.methodsS3_1.8.2 lifecycle_1.0.4 yaml_2.3.7
[49] SummarizedExperiment_1.32.0 rhdf5_2.46.1 SparseArray_1.2.2
[52] BiocFileCache_2.10.1 Rtsne_0.17 grid_4.3.2
[55] blob_1.2.4 promises_1.2.1 shinydashboard_0.7.2
[58] crayon_1.5.2 miniUI_0.1.1.1 lattice_0.22-5
[61] annotate_1.80.0 GenomicFeatures_1.54.1 KEGGREST_1.42.0
[64] pillar_1.9.0 GenomicRanges_1.54.1 rjson_0.2.21
[67] future.apply_1.11.0 codetools_0.2-19 fastmatch_1.1-4
[70] leiden_0.4.3.1 glue_1.6.2 data.table_1.14.10
[73] vctrs_0.6.5 png_0.1-8 spam_2.10-0
[76] cellranger_1.1.0 poweRlaw_0.70.6 gtable_0.3.4
[79] cachem_1.0.8 Signac_1.12.0 S4Arrays_1.2.0
[82] mime_0.12 pracma_2.4.4 survival_3.5-7
[85] gargle_1.5.2 RcppRoll_0.3.0 ellipsis_0.3.2
[88] fitdistrplus_1.1-11 ROCR_1.0-11 nlme_3.1-164
[91] bit64_4.0.5 progress_1.2.3 filelock_1.0.3
[94] RcppAnnoy_0.0.21 GenomeInfoDb_1.38.1 irlba_2.3.5.1
[97] KernSmooth_2.23-22 colorspace_2.1-0 seqLogo_1.68.0
[100] BiocGenerics_0.48.1 DBI_1.1.3 tidyselect_1.2.0
[103] bit_4.0.5 compiler_4.3.2 curl_5.2.0
[106] hdf5r_1.3.8 xml2_1.3.6 DelayedArray_0.28.0
[109] plotly_4.10.3 rtracklayer_1.62.0 scales_1.3.0
[112] caTools_1.18.2 lmtest_0.9-40 rappdirs_0.3.3
[115] digest_0.6.33 goftest_1.2-3 presto_1.0.0
[118] spatstat.utils_3.0-4 XVector_0.42.0 htmltools_0.5.7
[121] pkgconfig_2.0.3 MatrixGenerics_1.14.0 dbplyr_2.4.0
[124] fastmap_1.1.1 ensembldb_2.26.0 rlang_1.1.2
[127] htmlwidgets_1.6.4 shiny_1.8.0 zoo_1.8-12
[130] jsonlite_1.8.8 BiocParallel_1.36.0 R.oo_1.25.0
[133] RCurl_1.98-1.13 GenomeInfoDbData_1.2.11 dotCall64_1.1-1
[136] Rhdf5lib_1.24.0 munsell_0.5.0 reticulate_1.34.0
[139] stringi_1.8.2 zlibbioc_1.48.0 MASS_7.3-60
[142] plyr_1.8.9 parallel_4.3.2 listenv_0.9.0
[145] ggrepel_0.9.4 deldir_2.0-2 CNEr_1.38.0
[148] Biostrings_2.70.1 splines_4.3.2 tensor_1.5
[151] hms_1.1.3 BSgenome.Hsapiens.UCSC.hg38_1.4.5 igraph_1.6.0
[154] spatstat.geom_3.2-7 RcppHNSW_0.5.0 reshape2_1.4.4
[157] biomaRt_2.58.0 stats4_4.3.2 TFMPvalue_0.0.9
[160] XML_3.99-0.16 BiocManager_1.30.22 JASPAR2020_0.99.10
[163] tzdb_0.4.0 httpuv_1.6.13 RANN_2.6.1
[166] polyclip_1.10-6 future_1.33.0 scattermore_1.2
[169] xtable_1.8-4 restfulr_0.0.15 AnnotationFilter_1.26.0
[172] RSpectra_0.16-1 later_1.3.2 googledrive_2.1.1
[175] memoise_2.0.1 AnnotationDbi_1.64.1 GenomicAlignments_1.38.0
[178] IRanges_2.36.0 cluster_2.1.6 timechange_0.2.0
[181] globals_0.16.2

[dcollins15]

A solution is available here.