juicejulia
commented
6 months ago An update that the error seems to originate from some of the samples had way lower numbers of cells ( < 5,000 cells) compared with the rest of the samples (> 50,000 cells). Deleting those low cell number samples results in successfully sketching and downstream steps. Not exactly sure why...
igrabski
Hello, Thank you for developing and continuing to improve the wonderful Seurat package.
We are trying to use the sketch integration method on our CyTOF data. We have ~ 40 million cells from 47 donors, with 33 antibody channels. We got the following error when calling SketchData:
Calcuating Leverage Score Error in irlba(A = object, nv = 50, nu = 0, verbose = FALSE) : max(nu, nv) must be strictly less than min(nrow(A), ncol(A))
I think this error comes from that fact that we only have 33 dimensions. But I don't know whether there is a way to change the default value of the irlba internal function? In addition, what was confusing is that we were able to run the whole dataset a couple of months ago with the beta release of Seurat V5. Not sure what is changed. Below is my session info: R version 4.2.3 (2023-03-15) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Ubuntu 18.04.6 LTS
Matrix products: default BLAS/LAPACK: /home/jwang/anaconda3/envs/r_4.3.0/lib/libopenblasp-r0.3.26.so
locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
[3] 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
[7] LC_PAPER=en_US.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages: [1] stats graphics grDevices utils datasets methods base
other attached packages: [1] SeuratWrappers_0.3.19 Azimuth_0.5.0 shinyBS_0.61.1
[4] patchwork_1.2.0 ggrepel_0.9.5 ggplot2_3.4.4
[7] dplyr_1.1.4 BPCells_0.1.0 Seurat_5.0.2
[10] SeuratObject_5.0.1 sp_2.1-3
loaded via a namespace (and not attached): [1] rappdirs_0.3.3 rtracklayer_1.58.0
[3] scattermore_1.2 R.methodsS3_1.8.2
[5] tidyr_1.3.1 JASPAR2020_0.99.10
[7] bit64_4.0.5 irlba_2.3.5.1
[9] DelayedArray_0.24.0 R.utils_2.12.3
[11] data.table_1.15.0 KEGGREST_1.38.0
[13] TFBSTools_1.36.0 RCurl_1.98-1.14
[15] AnnotationFilter_1.22.0 generics_0.1.3
[17] BiocGenerics_0.44.0 GenomicFeatures_1.50.4
[19] callr_3.7.3 cowplot_1.1.3
[21] usethis_2.2.2 RSQLite_2.3.5
[23] RANN_2.6.1 future_1.33.1
[25] bit_4.0.5 tzdb_0.4.0
[27] spatstat.data_3.0-4 xml2_1.3.6
[29] httpuv_1.6.14 SummarizedExperiment_1.28.0
[31] DirichletMultinomial_1.40.0 gargle_1.5.2
[33] hms_1.1.3 promises_1.2.1
[35] fansi_1.0.6 restfulr_0.0.15
[37] progress_1.2.3 caTools_1.18.2
[39] dbplyr_2.4.0 igraph_1.5.1
[41] DBI_1.2.1 htmlwidgets_1.6.4
[43] spatstat.geom_3.2-8 googledrive_2.1.1
[45] stats4_4.2.3 purrr_1.0.2
[47] ellipsis_0.3.2 RSpectra_0.16-1
[49] annotate_1.76.0 biomaRt_2.54.1
[51] deldir_2.0-2 MatrixGenerics_1.10.0
[53] vctrs_0.6.5 Biobase_2.58.0
[55] remotes_2.4.2.1 SeuratDisk_0.0.0.9021
[57] ensembldb_2.22.0 ROCR_1.0-11
[59] abind_1.4-5 cachem_1.0.8
[61] withr_3.0.0 BSgenome.Hsapiens.UCSC.hg38_1.4.5 [63] BSgenome_1.66.3 progressr_0.14.0
[65] presto_1.0.0 sctransform_0.4.1
[67] GenomicAlignments_1.34.1 prettyunits_1.2.0
[69] goftest_1.2-3 cluster_2.1.6
[71] dotCall64_1.1-1 lazyeval_0.2.2
[73] seqLogo_1.64.0 crayon_1.5.2
[75] hdf5r_1.3.9 spatstat.explore_3.2-5
[77] pkgconfig_2.0.3 GenomeInfoDb_1.34.9
[79] pkgload_1.3.4 nlme_3.1-164
[81] ProtGenerics_1.30.0 devtools_2.4.5
[83] rlang_1.1.3 globals_0.16.2
[85] lifecycle_1.0.4 miniUI_0.1.1.1
[87] filelock_1.0.3 fastDummies_1.7.3
[89] BiocFileCache_2.6.1 rsvd_1.0.5
[91] SeuratData_0.2.2.9001 cellranger_1.1.0
[93] polyclip_1.10-6 RcppHNSW_0.5.0
[95] matrixStats_1.1.0 lmtest_0.9-40
[97] Matrix_1.6-5 Rhdf5lib_1.20.0
[99] zoo_1.8-12 processx_3.8.3
[101] ggridges_0.5.6 googlesheets4_1.1.1
[103] png_0.1-8 viridisLite_0.4.2
[105] rjson_0.2.21 bitops_1.0-7
[107] shinydashboard_0.7.2 R.oo_1.26.0
[109] KernSmooth_2.23-22 spam_2.10-0
[111] rhdf5filters_1.10.1 Biostrings_2.66.0
[113] blob_1.2.4 stringr_1.5.1
[115] parallelly_1.36.0 spatstat.random_3.2-2
[117] readr_2.1.5 S4Vectors_0.36.2
[119] CNEr_1.34.0 scales_1.3.0
[121] memoise_2.0.1 magrittr_2.0.3
[123] plyr_1.8.9 ica_1.0-3
[125] zlibbioc_1.44.0 compiler_4.2.3
[127] BiocIO_1.8.0 RColorBrewer_1.1-3
[129] fitdistrplus_1.1-11 Rsamtools_2.14.0
[131] cli_3.6.2 urlchecker_1.0.1
[133] XVector_0.38.0 listenv_0.9.1
[135] ps_1.7.6 pbapply_1.7-2
[137] MASS_7.3-60.0.1 tidyselect_1.2.0
[139] stringi_1.8.3 yaml_2.3.8
[141] grid_4.2.3 fastmatch_1.1-4
[143] EnsDb.Hsapiens.v86_2.99.0 tools_4.2.3
[145] future.apply_1.11.1 parallel_4.2.3
[147] TFMPvalue_0.0.9 gridExtra_2.3
[149] Rtsne_0.17 BiocManager_1.30.22
[151] digest_0.6.34 shiny_1.8.0
[153] pracma_2.4.4 Rcpp_1.0.12
[155] GenomicRanges_1.50.2 later_1.3.2
[157] RcppAnnoy_0.0.22 httr_1.4.7
[159] AnnotationDbi_1.60.2 colorspace_2.1-0
[161] XML_3.99-0.16.1 fs_1.6.3
[163] tensor_1.5 reticulate_1.34.0
[165] IRanges_2.32.0 splines_4.2.3
[167] uwot_0.1.16 RcppRoll_0.3.0
[169] spatstat.utils_3.0-4 sessioninfo_1.2.2
[171] plotly_4.10.4 xtable_1.8-4
[173] jsonlite_1.8.8 poweRlaw_0.80.0
[175] R6_2.5.1 profvis_0.3.8
[177] pillar_1.9.0 htmltools_0.5.7
[179] mime_0.12 glue_1.7.0
[181] fastmap_1.1.1 DT_0.31
[183] BiocParallel_1.32.6 codetools_0.2-19
[185] pkgbuild_1.4.3 Signac_1.12.0
[187] utf8_1.2.4 lattice_0.22-5
[189] spatstat.sparse_3.0-3 tibble_3.2.1
[191] curl_5.2.0 leiden_0.4.3.1
[193] gtools_3.9.5 shinyjs_2.1.0
[195] GO.db_3.16.0 survival_3.5-7
[197] desc_1.4.3 munsell_0.5.0
[199] rhdf5_2.42.1 GenomeInfoDbData_1.2.9
[201] reshape2_1.4.4 gtable_0.3.4