Error while making reference files

[BaluPai]

Hi,

Thanks for the RCTD pipeline. I have been trying to run RCTD using my own reference snRNAseq data and 10x Visium. I have been having trouble generating the input files. Getting various error messages, last one being: "> _reference<- Reference(counts, cell_types, nUMI) Error in .m2sparse(from, "dgC") : invalid type "character" in 'R_matrix_assparse' "

Could anyone share a thoughts/suggestions/code for this? I tried to adapt from here :https://rdrr.io/github/dmcable/RCTD/f/vignettes/spatial-transcriptomics.Rmd

Thanks!

sessionInfo() R version 4.2.0 (2022-04-22) Platform: x86_64-pc-linux-gnu (64-bit) Running under: CentOS Linux 7 (Core)

Matrix products: default BLAS/LAPACK: /hpc/packages/minerva-centos7/intel/parallel_studio_xe_2019/compilers_and_libraries_2019.0.117/linux/mkl/lib/intel64_lin/libmkl_gf_lp64.so

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

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

other attached packages: [1] DropletUtils_1.18.1 SingleCellExperiment_1.18.1 SummarizedExperiment_1.26.1 Biobase_2.56.0 GenomicRanges_1.48.0
[6] GenomeInfoDb_1.34.9 IRanges_2.32.0 S4Vectors_0.36.2 BiocGenerics_0.44.0 MatrixGenerics_1.10.0
[11] matrixStats_0.62.0 spacexr_2.2.1 EnhancedVolcano_1.14.0 magrittr_2.0.3 forcats_1.0.0
[16] stringr_1.5.1 purrr_1.0.1 readr_2.1.2 tidyr_1.2.0 tibble_3.2.1
[21] tidyverse_1.3.1 patchwork_1.1.1 dplyr_1.1.2 Seurat_5.0.1 ggrepel_0.9.1
[26] ggplot2_3.4.3 gtools_3.9.2.2 SeuratObject_5.0.1 sp_1.5-0

loaded via a namespace (and not attached): [1] utf8_1.2.3 spatstat.explore_3.0-6 reticulate_1.31 R.utils_2.12.0 tidyselect_1.2.0
[6] htmlwidgets_1.6.2 grid_4.2.0 BiocParallel_1.32.6 Rtsne_0.16 munsell_0.5.0
[11] codetools_0.2-18 ica_1.0-2 future_1.26.1 miniUI_0.1.1.1 withr_2.5.0
[16] spatstat.random_3.1-3 colorspace_2.0-3 progressr_0.10.1 rstudioapi_0.13 ROCR_1.0-11
[21] tensor_1.5 listenv_0.8.0 GenomeInfoDbData_1.2.8 polyclip_1.10-0 rhdf5_2.40.0
[26] parallelly_1.32.0 vctrs_0.6.3 generics_0.1.3 R6_2.5.1 doParallel_1.0.17
[31] locfit_1.5-9.5 bitops_1.0-7 rhdf5filters_1.8.0 spatstat.utils_3.0-1 DelayedArray_0.24.0
[36] assertthat_0.2.1 promises_1.2.0.1 scales_1.2.1 gtable_0.3.0 beachmat_2.14.2
[41] globals_0.15.1 goftest_1.2-3 spam_2.8-0 rlang_1.1.1 splines_4.2.0
[46] lazyeval_0.2.2 spatstat.geom_3.0-6 broom_1.0.5 reshape2_1.4.4 abind_1.4-5
[51] modelr_0.1.8 backports_1.4.1 httpuv_1.6.5 tools_4.2.0 ellipsis_0.3.2
[56] RColorBrewer_1.1-3 ggridges_0.5.3 Rcpp_1.0.10 plyr_1.8.7 sparseMatrixStats_1.10.0 [61] zlibbioc_1.42.0 RCurl_1.98-1.7 deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[66] zoo_1.8-10 haven_2.5.0 cluster_2.1.4 fs_1.6.3 data.table_1.14.2
[71] RSpectra_0.16-1 scattermore_1.2 lmtest_0.9-40 reprex_2.0.1 RANN_2.6.1
[76] fitdistrplus_1.1-8 hms_1.1.1 mime_0.12 xtable_1.8-4 readxl_1.4.0
[81] fastDummies_1.7.3 gridExtra_2.3 compiler_4.2.0 KernSmooth_2.23-20 crayon_1.5.1
[86] R.oo_1.25.0 htmltools_0.5.5 later_1.3.0 tzdb_0.3.0 lubridate_1.8.0
[91] DBI_1.1.3 dbplyr_2.2.1 MASS_7.3-56 Matrix_1.6-4 cli_3.6.1
[96] R.methodsS3_1.8.2 parallel_4.2.0 dotCall64_1.0-1 igraph_1.4.1 pkgconfig_2.0.3
[101] scuttle_1.6.2 plotly_4.10.0 spatstat.sparse_3.0-0 xml2_1.3.3 foreach_1.5.2
[106] dqrng_0.3.0 XVector_0.36.0 rvest_1.0.2 digest_0.6.29 sctransform_0.4.1
[111] RcppAnnoy_0.0.19 spatstat.data_3.0-0 cellranger_1.1.0 leiden_0.4.2 edgeR_3.38.1
[116] uwot_0.1.14 DelayedMatrixStats_1.18.2 shiny_1.7.1 lifecycle_1.0.3 nlme_3.1-157
[121] jsonlite_1.8.0 Rhdf5lib_1.18.2 limma_3.54.2 viridisLite_0.4.0 fansi_1.0.3
[126] pillar_1.9.0 lattice_0.20-45 fastmap_1.1.0 httr_1.4.3 survival_3.3-1
[131] glue_1.6.2 png_0.1-7 iterators_1.0.14 stringi_1.7.6 HDF5Array_1.24.2
[136] RcppHNSW_0.3.0 irlba_2.3.5 future.apply_1.9.0

[BaluPai]

Solved!