__STDOUT LOG__ Job runs on fgcz-h-901 at /scratch/o5495_o5444_ScSeurat_2024-12-05--09-59-39_Undifferentiated_lung_cells_temp1767852 Starting EzAppScSeurat ScSeurat o5495_o5444_ScSeurat_2024-12-05--09-59-39_Undifferentiated_lung_cells_temp1767852 2024-12-05 09:59:48 Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Tabula_Muris... Done. Querying Tabula_Sapiens... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Querying CellMarker_Augmented_2021... Done. Querying Allen_Brain_Atlas_10x_scRNA_2021... Done. Querying Human_Gene_Atlas... Done. Querying Mouse_Gene_Atlas... Done. Parsing results... Done. INFO [2024-12-05 10:26:02] Skipping pathway and TF activity ezRun tag: 733d7a85c98ed8bb0732c178986b67ce4584e581 ezRun github link: https://github.com/uzh/ezRun/tree/733d7a85c98ed8bb0732c178986b67ce4584e581 R version 4.4.0 (2024-04-24) Platform: x86_64-pc-linux-gnu Running under: Debian GNU/Linux 12 (bookworm) Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0 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 time zone: Europe/Zurich tzcode source: system (glibc) attached base packages: [1] stats4 stats graphics grDevices utils datasets methods [8] base other attached packages: [1] doRNG_1.8.6 foreach_1.5.2 [3] htmltools_0.5.8.1 DT_0.33 [5] UpSetR_1.4.0 cellxgenedp_1.8.0 [7] SCpubr_2.0.2 patchwork_1.2.0 [9] plotly_4.10.4 RColorBrewer_1.1-3 [11] cowplot_1.1.3 viridis_0.6.5 [13] viridisLite_0.4.2 pheatmap_1.0.12 [15] NMF_0.27 cluster_2.1.6 [17] rngtools_1.5.2 registry_0.5-1 [19] kableExtra_1.4.0 clustree_0.5.1 [21] ggraph_2.2.1 SoupX_1.6.2 [23] celda_1.20.0 scran_1.32.0 [25] scater_1.32.0 future_1.33.2 [27] Azimuth_0.5.0 shinyBS_0.62 [29] decoupleR_2.10.0 enrichR_3.2 [31] DropletUtils_1.24.0 scuttle_1.14.0 [33] BiocParallel_1.38.0 scDblFinder_1.18.0 [35] SingleCellExperiment_1.26.0 Seurat_5.1.0 [37] SeuratObject_5.0.2 sp_2.1-4 [39] SingleR_2.6.0 SummarizedExperiment_1.34.0 [41] GSEABase_1.66.0 graph_1.82.0 [43] annotate_1.82.0 XML_3.99-0.16.1 [45] AnnotationDbi_1.66.0 Biobase_2.64.0 [47] AUCell_1.26.0 HDF5Array_1.32.0 [49] rhdf5_2.48.0 DelayedArray_0.30.1 [51] SparseArray_1.4.8 S4Arrays_1.4.1 [53] abind_1.4-5 MatrixGenerics_1.16.0 [55] matrixStats_1.3.0 Matrix_1.7-0 [57] ezRun_3.19.1 lubridate_1.9.3 [59] forcats_1.0.0 stringr_1.5.1 [61] dplyr_1.1.4 purrr_1.0.2 [63] readr_2.1.5 tidyr_1.3.1 [65] tibble_3.2.1 ggplot2_3.5.1 [67] tidyverse_2.0.0 GenomicRanges_1.56.1 [69] Biostrings_2.72.1 GenomeInfoDb_1.40.1 [71] XVector_0.44.0 IRanges_2.38.0 [73] S4Vectors_0.42.0 BiocGenerics_0.50.0 [75] data.table_1.15.4 loaded via a namespace (and not attached): [1] igraph_2.0.3 ica_1.0-3 [3] zlibbioc_1.50.0 tidyselect_1.2.1 [5] bit_4.0.5 doParallel_1.0.17 [7] lattice_0.22-6 rjson_0.2.21 [9] blob_1.2.4 parallel_4.4.0 [11] seqLogo_1.70.0 png_0.1-8 [13] cli_3.6.2 ProtGenerics_1.36.0 [15] goftest_1.2-3 gargle_1.5.2 [17] BiocIO_1.14.0 glmGamPoi_1.16.0 [19] kernlab_0.9-32 bluster_1.14.0 [21] BiocNeighbors_1.22.0 Signac_1.13.0 [23] uwot_0.2.2 curl_5.2.1 [25] mime_0.12 evaluate_0.24.0 [27] leiden_0.4.3.1 stringi_1.8.4 [29] backports_1.5.0 httpuv_1.6.15 [31] magrittr_2.0.3 rappdirs_0.3.3 [33] splines_4.4.0 RcppRoll_0.3.0 [35] sctransform_0.4.1 ggbeeswarm_0.7.2 [37] DBI_1.2.3 jquerylib_0.1.4 [39] withr_3.0.0 systemfonts_1.1.0 [41] xgboost_1.7.7.1 lmtest_0.9-40 [43] tidygraph_1.3.1 formatR_1.14 [45] rtracklayer_1.64.0 htmlwidgets_1.6.4 [47] fs_1.6.4 segmented_2.1-0 [49] ggrepel_0.9.5 labeling_0.4.3 [51] cellranger_1.1.0 mixtools_2.0.0 [53] reticulate_1.37.0 zoo_1.8-12 [55] JASPAR2020_0.99.10 knitr_1.47 [57] TFBSTools_1.42.0 UCSC.utils_1.0.0 [59] TFMPvalue_0.0.9 timechange_0.3.0 [61] fansi_1.0.6 caTools_1.18.2 [63] grid_4.4.0 pwalign_1.0.0 [65] R.oo_1.26.0 poweRlaw_0.80.0 [67] RSpectra_0.16-1 irlba_2.3.5.1 [69] ggrastr_1.0.2 fastDummies_1.7.3 [71] lazyeval_0.2.2 yaml_2.3.8 [73] survival_3.7-0 scattermore_1.2 [75] crayon_1.5.2 RcppAnnoy_0.0.22 [77] progressr_0.14.0 tweenr_2.0.3 [79] later_1.3.2 ggridges_0.5.6 [81] codetools_0.2-20 KEGGREST_1.44.0 [83] Rtsne_0.17 limma_3.60.2 [85] Rsamtools_2.20.0 pkgconfig_2.0.3 [87] xml2_1.3.6 GenomicAlignments_1.40.0 [89] spatstat.sparse_3.0-3 BSgenome_1.72.0 [91] gridBase_0.4-7 xtable_1.8-4 [93] highr_0.11 plyr_1.8.9 [95] httr_1.4.7 tools_4.4.0 [97] globals_0.16.3 checkmate_2.3.1 [99] beeswarm_0.4.0 nlme_3.1-165 [101] futile.logger_1.4.3 lambda.r_1.2.4 [103] hdf5r_1.3.10 crosstalk_1.2.1 [105] shinyjs_2.1.0 assertthat_0.2.1 [107] digest_0.6.35 farver_2.1.2 [109] tzdb_0.4.0 AnnotationFilter_1.28.0 [111] reshape2_1.4.4 WriteXLS_6.6.0 [113] DirichletMultinomial_1.46.0 glue_1.7.0 [115] cachem_1.1.0 polyclip_1.10-6 [117] rjsoncons_1.3.0 generics_0.1.3 [119] googledrive_2.1.1 presto_1.0.0 [121] parallelly_1.37.1 statmod_1.5.0 [123] RcppHNSW_0.6.0 ScaledMatrix_1.12.0 [125] pbapply_1.7-2 spam_2.10-0 [127] dqrng_0.4.1 utf8_1.2.4 [129] graphlayouts_1.1.1 gtools_3.9.5 [131] readxl_1.4.3 RcppEigen_0.3.4.0.0 [133] gridExtra_2.3 shiny_1.9.1 [135] GenomeInfoDbData_1.2.12 R.utils_2.12.3 [137] rhdf5filters_1.16.0 RCurl_1.98-1.14 [139] memoise_2.0.1 rmarkdown_2.27 [141] scales_1.3.0 R.methodsS3_1.8.2 [143] googlesheets4_1.1.1 svglite_2.1.3 [145] RANN_2.6.1 spatstat.data_3.0-4 [147] rstudioapi_0.16.0 spatstat.utils_3.0-5 [149] hms_1.1.3 fitdistrplus_1.1-11 [151] munsell_0.5.1 colorspace_2.1-1 [153] rlang_1.1.4 DelayedMatrixStats_1.26.0 [155] sparseMatrixStats_1.16.0 dotCall64_1.1-1 [157] shinydashboard_0.7.2 ggforce_0.4.2 [159] xfun_0.45 CNEr_1.40.0 [161] iterators_1.0.14 MCMCprecision_0.4.0 [163] EnsDb.Hsapiens.v86_2.99.0 Rhdf5lib_1.26.0 [165] futile.options_1.0.1 bitops_1.0-7 [167] promises_1.3.0 RSQLite_2.3.7 [169] GO.db_3.19.1 compiler_4.4.0 [171] writexl_1.5.0 beachmat_2.20.0 [173] listenv_0.9.1 BSgenome.Hsapiens.UCSC.hg38_1.4.5 [175] Rcpp_1.0.12 edgeR_4.2.0 [177] BiocSingular_1.20.0 tensor_1.5 [179] MASS_7.3-61 spatstat.random_3.2-3 [181] R6_2.5.1 fastmap_1.2.0 [183] fastmatch_1.1-4 vipor_0.4.7 [185] ensembldb_2.28.0 ROCR_1.0-11 [187] SeuratDisk_0.0.0.9021 rsvd_1.0.5 [189] gtable_0.3.5 KernSmooth_2.23-24 [191] miniUI_0.1.1.1 deldir_2.0-4 [193] bit64_4.0.5 spatstat.explore_3.2-7 [195] lifecycle_1.0.4 restfulr_0.0.15 [197] sass_0.4.9 vctrs_0.6.5 [199] spatstat.geom_3.2-9 SeuratData_0.2.2.9001 [201] future.apply_1.11.2 pracma_2.4.4 [203] bslib_0.7.0 pillar_1.9.0 [205] GenomicFeatures_1.56.0 doMC_1.3.8 [207] metapod_1.12.0 locfit_1.5-9.9 [209] combinat_0.0-8 jsonlite_1.8.8 Finished EzAppScSeurat ScSeurat o5495_o5444_ScSeurat_2024-12-05--09-59-39_Undifferentiated_lung_cells_temp1767852 2024-12-05 10:32:08 [1] "Success" __SCRIPT END__ __STDERR LOG__ Loading required package: data.table Loading required package: Biostrings Loading required package: BiocGenerics Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:stats’: IQR, mad, sd, var, xtabs The following objects are masked from ‘package:base’: anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Loading required package: stats4 Attaching package: ‘S4Vectors’ The following objects are masked from ‘package:data.table’: first, second The following object is masked from ‘package:utils’: findMatches The following objects are masked from ‘package:base’: expand.grid, I, unname Loading required package: IRanges Attaching package: ‘IRanges’ The following object is masked from ‘package:data.table’: shift Loading required package: XVector Loading required package: GenomeInfoDb Attaching package: ‘Biostrings’ The following object is masked from ‘package:base’: strsplit Loading required package: GenomicRanges Loading required package: tidyverse ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ✔ dplyr 1.1.4 ✔ readr 2.1.5 ✔ forcats 1.0.0 ✔ stringr 1.5.1 ✔ ggplot2 3.5.1 ✔ tibble 3.2.1 ✔ lubridate 1.9.3 ✔ tidyr 1.3.1 ✔ purrr 1.0.2 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ lubridate::%within%() masks IRanges::%within%() ✖ dplyr::between() masks data.table::between() ✖ dplyr::collapse() masks Biostrings::collapse(), IRanges::collapse() ✖ dplyr::combine() masks BiocGenerics::combine() ✖ purrr::compact() masks XVector::compact() ✖ dplyr::desc() masks IRanges::desc() ✖ tidyr::expand() masks S4Vectors::expand() ✖ dplyr::filter() masks stats::filter() ✖ dplyr::first() masks S4Vectors::first(), data.table::first() ✖ lubridate::hour() masks data.table::hour() ✖ lubridate::isoweek() masks data.table::isoweek() ✖ dplyr::lag() masks stats::lag() ✖ dplyr::last() masks data.table::last() ✖ lubridate::mday() masks data.table::mday() ✖ lubridate::minute() masks data.table::minute() ✖ lubridate::month() masks data.table::month() ✖ ggplot2::Position() masks BiocGenerics::Position(), base::Position() ✖ lubridate::quarter() masks data.table::quarter() ✖ purrr::reduce() masks GenomicRanges::reduce(), IRanges::reduce() ✖ dplyr::rename() masks S4Vectors::rename() ✖ lubridate::second() masks S4Vectors::second(), data.table::second() ✖ lubridate::second<-() masks S4Vectors::second<-() ✖ dplyr::slice() masks XVector::slice(), IRanges::slice() ✖ purrr::transpose() masks data.table::transpose() ✖ lubridate::wday() masks data.table::wday() ✖ lubridate::week() masks data.table::week() ✖ lubridate::yday() masks data.table::yday() ✖ lubridate::year() masks data.table::year() ℹ Use the conflicted package () to force all conflicts to become errors unknown param: partition unknown param: tissue unknown param: Azimuth unknown param: SingleR unknown param: cellxgeneUrl unknown param: cellxgeneLabel unknown param: sushi_app unknown param: isLastJob Loading required package: DelayedArray Loading required package: Matrix Attaching package: ‘Matrix’ The following objects are masked from ‘package:tidyr’: expand, pack, unpack The following object is masked from ‘package:S4Vectors’: expand Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: ‘matrixStats’ The following object is masked from ‘package:dplyr’: count Attaching package: ‘MatrixGenerics’ The following objects are masked from ‘package:matrixStats’: colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: S4Arrays Loading required package: abind Attaching package: ‘S4Arrays’ The following object is masked from ‘package:abind’: abind The following object is masked from ‘package:base’: rowsum Loading required package: SparseArray Attaching package: ‘DelayedArray’ The following object is masked from ‘package:purrr’: simplify The following objects are masked from ‘package:base’: apply, scale, sweep Loading required package: rhdf5 Attaching package: ‘HDF5Array’ The following object is masked from ‘package:rhdf5’: h5ls Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: ‘Biobase’ The following object is masked from ‘package:SparseArray’: rowMedians The following object is masked from ‘package:MatrixGenerics’: rowMedians The following objects are masked from ‘package:matrixStats’: anyMissing, rowMedians Loading required package: annotate Loading required package: AnnotationDbi Attaching package: ‘AnnotationDbi’ The following object is masked from ‘package:dplyr’: select Loading required package: XML Loading required package: graph Attaching package: ‘graph’ The following object is masked from ‘package:XML’: addNode The following object is masked from ‘package:stringr’: boundary The following object is masked from ‘package:Biostrings’: complement Loading required package: SummarizedExperiment Loading required package: SeuratObject Loading required package: sp Attaching package: ‘sp’ The following object is masked from ‘package:IRanges’: %over% Attaching package: ‘SeuratObject’ The following object is masked from ‘package:SummarizedExperiment’: Assays The following object is masked from ‘package:GSEABase’: intersect The following object is masked from ‘package:GenomicRanges’: intersect The following object is masked from ‘package:Biostrings’: intersect The following object is masked from ‘package:GenomeInfoDb’: intersect The following object is masked from ‘package:IRanges’: intersect The following object is masked from ‘package:S4Vectors’: intersect The following object is masked from ‘package:BiocGenerics’: intersect The following objects are masked from ‘package:base’: intersect, t Attaching package: ‘Seurat’ The following object is masked from ‘package:SummarizedExperiment’: Assays The following object is masked from ‘package:ezRun’: SingleCorPlot Loading required package: SingleCellExperiment Welcome to enrichR Checking connection ... Enrichr ... Connection is Live! FlyEnrichr ... Connection is Live! WormEnrichr ... Connection is Live! YeastEnrichr ... Connection is Live! FishEnrichr ... Connection is Live! OxEnrichr ... Connection is Live! Attaching package: ‘decoupleR’ The following object is masked from ‘package:data.table’: := Registered S3 method overwritten by 'SeuratDisk': method from as.sparse.H5Group Seurat Attaching shinyBS Loading required package: future Assuming the input to be a matrix of counts or expected counts. Clustering cells... 9 clusters Creating ~4532 artificial doublets... Dimensional reduction Evaluating kNN... Training model... iter=0, 220 cells excluded from training. iter=1, 202 cells excluded from training. iter=2, 208 cells excluded from training. Threshold found:0.34 218 (3.8%) doublets called Loading required package: scran Warning: Cannot find a parent environment called Seurat Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' This message will be shown once per session 10:20:34 UMAP embedding parameters a = 0.9922 b = 1.112 10:20:34 Read 5446 rows and found 20 numeric columns 10:20:34 Using Annoy for neighbor search, n_neighbors = 30 10:20:34 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:20:34 Writing NN index file to temp file /tmp/RtmpXeHBY9/file1af9c75c2c7be1 10:20:34 Searching Annoy index using 4 threads, search_k = 3000 10:20:35 Annoy recall = 100% 10:20:37 Commencing smooth kNN distance calibration using 4 threads with target n_neighbors = 30 10:20:41 Initializing from normalized Laplacian + noise (using RSpectra) 10:20:41 Commencing optimization for 500 epochs, with 223366 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:20:49 Optimization finished Attaching package: 'celda' The following objects are masked from 'package:scater': normalizeCounts, plotHeatmap The following object is masked from 'package:scuttle': normalizeCounts The following object is masked from 'package:ezRun': selectFeatures The following object is masked from 'package:S4Vectors': params -------------------------------------------------- Starting DecontX -------------------------------------------------- Thu Dec 5 10:20:53 2024 .. Analyzing all cells Thu Dec 5 10:20:53 2024 .... Generating UMAP Thu Dec 5 10:21:25 2024 .... Estimating contamination Thu Dec 5 10:21:29 2024 ...... Completed iteration: 10 | converge: 0.02182 Thu Dec 5 10:21:31 2024 ...... Completed iteration: 20 | converge: 0.01495 Thu Dec 5 10:21:34 2024 ...... Completed iteration: 30 | converge: 0.01009 Thu Dec 5 10:21:36 2024 ...... Completed iteration: 40 | converge: 0.006617 Thu Dec 5 10:21:39 2024 ...... Completed iteration: 50 | converge: 0.005275 Thu Dec 5 10:21:42 2024 ...... Completed iteration: 60 | converge: 0.004179 Thu Dec 5 10:21:44 2024 ...... Completed iteration: 70 | converge: 0.00333 Thu Dec 5 10:21:47 2024 ...... Completed iteration: 80 | converge: 0.002683 Thu Dec 5 10:21:50 2024 ...... Completed iteration: 90 | converge: 0.002192 Thu Dec 5 10:21:52 2024 ...... Completed iteration: 100 | converge: 0.001816 Thu Dec 5 10:21:55 2024 ...... Completed iteration: 110 | converge: 0.001526 Thu Dec 5 10:21:57 2024 ...... Completed iteration: 120 | converge: 0.001327 Thu Dec 5 10:22:00 2024 ...... Completed iteration: 130 | converge: 0.001165 Thu Dec 5 10:22:03 2024 ...... Completed iteration: 140 | converge: 0.001051 Thu Dec 5 10:22:04 2024 ...... Completed iteration: 145 | converge: 0.0009979 Thu Dec 5 10:22:04 2024 .. Calculating final decontaminated matrix -------------------------------------------------- Completed DecontX. Total time: 1.246554 mins -------------------------------------------------- 236 genes passed tf-idf cut-off and 0 soup quantile filter. Taking the top 0. Error in autoEstCont(sc, tfidfMin = tfidfMin, forceAccept = T, doPlot = FALSE) : No plausible marker genes found. Is the channel low complexity (see help)? If not, reduce tfidfMin or soupQuantile 594 genes passed tf-idf cut-off and 3 soup quantile filter. Taking the top 3. Using 16 independent estimates of rho. Estimated global rho of 0.05 Expanding counts from 13 clusters to 5446 cells. Calculating cluster 0 Calculating cluster 1 Calculating cluster 2 Calculating cluster 3 Calculating cluster 4 Calculating cluster 5 Calculating cluster 6 Calculating cluster 7 Calculating cluster 8 Calculating cluster 9 Calculating cluster 10 Calculating cluster 11 Calculating cluster 12 Warning in .AUCell_calcAUC(geneSets = geneSets, rankings = rankings, nCores = nCores, : The following gene sets will be excluded from the analysis(less than 20% of their genes are available): Airway secretory cell, Alveolar pneumocyte Type II, Amacrine cell, Anti-tumor immune cell, CD34+ granulocytic progenitor cell, CD40LG+ T helper cell, Chief cell, Circulating natural killer cell, Clara cell, Conventional CD4 T cell, Cytotoxic CD4+ T2 cell, Effector natural killer cell, Elongating cell, Ensheathing glial cell, Gamma cell, IL7R T helper cell, Iris puttative stem cell, KLRF+ natural killer cell, Langerhans cell, Memory double-positive T cell, Metaplastic-stem cell, Monocyte-derived myeloid cell, Naive IL7R+ T cell, Negative mesenchymal stem cell, Non-β endocrine cell, Oligodendroglial cell, Pan leukocyte, Parietal cell, Pulmonary neuroendocrine cell, Somatotroph cell, Stem-cell-derived-beta cell, Sweat gland cell, T2-MZP Regulatory B cell, Terminal effector CD8+ T cell, Tissue resident macrophage, Tumor-associated microglia cell, Ventral medial ganglionic eminence precu [... truncated] Warning messages: 1: In autoEstCont(sc, tfidfMin = tfidfMin, forceAccept = T, doPlot = FALSE) : Fewer than 10 marker genes found. Is this channel low complexity (see help)? If not, consider reducing tfidfMin or soupQuantile 2: In sparseMatrix(i = out@i[w] + 1, j = out@j[w] + 1, x = out@x[w], : 'giveCsparse' is deprecated; setting repr="T" for you