__STDOUT LOG__ Job runs on fgcz-h-166 at /scratch/o5495_o5444_ScSeuratCombine_CCA_2024-12-06--17-30-56_temp1436484 Starting EzAppScSeuratCombine SCReportMultipleSamplesSeurat o5495_o5444_ScSeuratCombine_CCA_2024-12-06--17-30-56_temp1436484 2024-12-06 17:31:03 [1] 1 [1] 2 Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Querying PanglaoDB_Augmented_2021... Done. Parsing results... Done. INFO [2024-12-06 17:45:07] Skipping pathway and TF activity ezRun tag: daaaaae3a45d21cdc146b52a9dd92b4db2744530 ezRun github link: https://github.com/uzh/ezRun/tree/daaaaae3a45d21cdc146b52a9dd92b4db2744530 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] htmltools_0.5.8.1 DT_0.33 [3] scater_1.32.0 SingleR_2.6.0 [5] plotly_4.10.4 RColorBrewer_1.1-3 [7] scran_1.32.0 scuttle_1.14.0 [9] cowplot_1.1.3 viridis_0.6.5 [11] viridisLite_0.4.2 pheatmap_1.0.12 [13] NMF_0.27 cluster_2.1.6 [15] rngtools_1.5.2 registry_0.5-1 [17] kableExtra_1.4.0 clustree_0.5.1 [19] ggraph_2.2.1 BiocParallel_1.38.0 [21] Azimuth_0.5.0 shinyBS_0.62 [23] decoupleR_2.10.0 enrichR_3.2 [25] AUCell_1.26.0 SingleCellExperiment_1.26.0 [27] SummarizedExperiment_1.34.0 Biobase_2.64.0 [29] HDF5Array_1.32.0 rhdf5_2.48.0 [31] DelayedArray_0.30.1 SparseArray_1.4.8 [33] S4Arrays_1.4.1 abind_1.4-5 [35] MatrixGenerics_1.16.0 matrixStats_1.3.0 [37] Matrix_1.7-0 rlist_0.4.6.2 [39] Seurat_5.1.0 SeuratObject_5.0.2 [41] sp_2.1-4 ezRun_3.19.1 [43] lubridate_1.9.3 forcats_1.0.0 [45] stringr_1.5.1 dplyr_1.1.4 [47] purrr_1.0.2 readr_2.1.5 [49] tidyr_1.3.1 tibble_3.2.1 [51] ggplot2_3.5.1 tidyverse_2.0.0 [53] GenomicRanges_1.56.1 Biostrings_2.72.1 [55] GenomeInfoDb_1.40.1 XVector_0.44.0 [57] IRanges_2.38.0 S4Vectors_0.42.0 [59] BiocGenerics_0.50.0 data.table_1.15.4 loaded via a namespace (and not attached): [1] R.methodsS3_1.8.2 GSEABase_1.66.0 [3] poweRlaw_0.80.0 goftest_1.2-3 [5] vctrs_0.6.5 spatstat.random_3.2-3 [7] digest_0.6.35 png_0.1-8 [9] ggrepel_0.9.5 deldir_2.0-4 [11] parallelly_1.37.1 MASS_7.3-61 [13] Signac_1.13.0 reshape2_1.4.4 [15] foreach_1.5.2 httpuv_1.6.15 [17] withr_3.0.0 ggrastr_1.0.2 [19] xfun_0.45 survival_3.7-0 [21] EnsDb.Hsapiens.v86_2.99.0 memoise_2.0.1 [23] ggbeeswarm_0.7.2 systemfonts_1.1.0 [25] zoo_1.8-12 gtools_3.9.5 [27] pbapply_1.7-2 R.oo_1.26.0 [29] KEGGREST_1.44.0 promises_1.3.0 [31] httr_1.4.7 restfulr_0.0.15 [33] globals_0.16.3 fitdistrplus_1.1-11 [35] rhdf5filters_1.16.0 rstudioapi_0.16.0 [37] UCSC.utils_1.0.0 miniUI_0.1.1.1 [39] generics_0.1.3 curl_5.2.1 [41] zlibbioc_1.50.0 ScaledMatrix_1.12.0 [43] polyclip_1.10-6 GenomeInfoDbData_1.2.12 [45] doParallel_1.0.17 xtable_1.8-4 [47] pracma_2.4.4 evaluate_0.24.0 [49] hms_1.1.3 irlba_2.3.5.1 [51] colorspace_2.1-1 hdf5r_1.3.10 [53] ROCR_1.0-11 readxl_1.4.3 [55] reticulate_1.37.0 spatstat.data_3.0-4 [57] magrittr_2.0.3 lmtest_0.9-40 [59] later_1.3.2 lattice_0.22-6 [61] glmGamPoi_1.16.0 spatstat.geom_3.2-9 [63] future.apply_1.11.2 scattermore_1.2 [65] XML_3.99-0.16.1 RcppAnnoy_0.0.22 [67] pillar_1.9.0 nlme_3.1-165 [69] iterators_1.0.14 pwalign_1.0.0 [71] beachmat_2.20.0 gridBase_0.4-7 [73] caTools_1.18.2 compiler_4.4.0 [75] RSpectra_0.16-1 stringi_1.8.4 [77] tensor_1.5 GenomicAlignments_1.40.0 [79] plyr_1.8.9 crayon_1.5.2 [81] BiocIO_1.14.0 googledrive_2.1.1 [83] locfit_1.5-9.9 graphlayouts_1.1.1 [85] bit_4.0.5 fastmatch_1.1-4 [87] codetools_0.2-20 BiocSingular_1.20.0 [89] crosstalk_1.2.1 bslib_0.7.0 [91] SeuratData_0.2.2.9001 mime_0.12 [93] splines_4.4.0 Rcpp_1.0.12 [95] fastDummies_1.7.3 sparseMatrixStats_1.16.0 [97] cellranger_1.1.0 knitr_1.47 [99] blob_1.2.4 utf8_1.2.4 [101] seqLogo_1.70.0 AnnotationFilter_1.28.0 [103] WriteXLS_6.6.0 fs_1.6.4 [105] checkmate_2.3.1 listenv_0.9.1 [107] DelayedMatrixStats_1.26.0 statmod_1.5.0 [109] tzdb_0.4.0 svglite_2.1.3 [111] tweenr_2.0.3 pkgconfig_2.0.3 [113] tools_4.4.0 cachem_1.1.0 [115] RSQLite_2.3.7 DBI_1.2.3 [117] fastmap_1.2.0 rmarkdown_2.27 [119] scales_1.3.0 grid_4.4.0 [121] ica_1.0-3 shinydashboard_0.7.2 [123] Rsamtools_2.20.0 sass_0.4.9 [125] patchwork_1.2.0 dotCall64_1.1-1 [127] graph_1.82.0 varhandle_2.0.6 [129] RANN_2.6.1 farver_2.1.2 [131] tidygraph_1.3.1 yaml_2.3.8 [133] rtracklayer_1.64.0 cli_3.6.2 [135] writexl_1.5.0 leiden_0.4.3.1 [137] lifecycle_1.0.4 uwot_0.2.2 [139] backports_1.5.0 bluster_1.14.0 [141] lambda.r_1.2.4 presto_1.0.0 [143] BSgenome.Hsapiens.UCSC.hg38_1.4.5 annotate_1.82.0 [145] timechange_0.3.0 gtable_0.3.5 [147] rjson_0.2.21 ggridges_0.5.6 [149] progressr_0.14.0 parallel_4.4.0 [151] limma_3.60.2 edgeR_4.2.0 [153] jsonlite_1.8.8 RcppHNSW_0.6.0 [155] TFBSTools_1.42.0 bitops_1.0-7 [157] bit64_4.0.5 Rtsne_0.17 [159] BiocNeighbors_1.22.0 spatstat.utils_3.0-5 [161] CNEr_1.40.0 futile.options_1.0.1 [163] highr_0.11 metapod_1.12.0 [165] jquerylib_0.1.4 dqrng_0.4.1 [167] shinyjs_2.1.0 SeuratDisk_0.0.0.9021 [169] R.utils_2.12.3 lazyeval_0.2.2 [171] shiny_1.9.1 GO.db_3.19.1 [173] sctransform_0.4.1 rappdirs_0.3.3 [175] formatR_1.14 ensembldb_2.28.0 [177] glue_1.7.0 TFMPvalue_0.0.9 [179] spam_2.10-0 googlesheets4_1.1.1 [181] RCurl_1.98-1.14 BSgenome_1.72.0 [183] futile.logger_1.4.3 gridExtra_2.3 [185] JASPAR2020_0.99.10 igraph_2.0.3 [187] R6_2.5.1 labeling_0.4.3 [189] RcppRoll_0.3.0 GenomicFeatures_1.56.0 [191] Rhdf5lib_1.26.0 gargle_1.5.2 [193] DirichletMultinomial_1.46.0 vipor_0.4.7 [195] tidyselect_1.2.1 ProtGenerics_1.36.0 [197] ggforce_0.4.2 xml2_1.3.6 [199] AnnotationDbi_1.66.0 future_1.33.2 [201] rsvd_1.0.5 munsell_0.5.1 [203] KernSmooth_2.23-24 htmlwidgets_1.6.4 [205] rlang_1.1.4 spatstat.sparse_3.0-3 [207] spatstat.explore_3.2-7 fansi_1.0.6 [209] beeswarm_0.4.0 Finished EzAppScSeuratCombine SCReportMultipleSamplesSeurat o5495_o5444_ScSeuratCombine_CCA_2024-12-06--17-30-56_temp1436484 2024-12-06 17:49:10 [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: SingleR unknown param: additionalFactors unknown param: STACASAnnotationFile unknown param: sushi_app unknown param: isLastJob 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: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:ezRun’: SingleCorPlot Attaching package: ‘rlist’ The following object is masked from ‘package:S4Vectors’: List 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 Attaching package: ‘SummarizedExperiment’ The following object is masked from ‘package:Seurat’: Assays The following object is masked from ‘package:SeuratObject’: Assays 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 Running SCTransform on assay: RNA Running SCTransform on layer: counts vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. Variance stabilizing transformation of count matrix of size 16028 by 5446 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 5000 cells Found 58 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 16028 genes Computing corrected count matrix for 16028 genes Calculating gene attributes Wall clock passed: Time difference of 22.1747 secs Determine variable features Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Getting residuals for block 1(of 2) for counts dataset Getting residuals for block 2(of 2) for counts dataset Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Finished calculating residuals for counts Set default assay to SCT Running SCTransform on assay: RNA Running SCTransform on layer: counts vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. Variance stabilizing transformation of count matrix of size 16759 by 9298 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 5000 cells Found 177 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 16759 genes Computing corrected count matrix for 16759 genes Calculating gene attributes Wall clock passed: Time difference of 26.98039 secs Determine variable features Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Getting residuals for block 1(of 2) for counts dataset Getting residuals for block 2(of 2) for counts dataset Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Finished calculating residuals for counts Set default assay to SCT 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 17:35:18 UMAP embedding parameters a = 0.9922 b = 1.112 17:35:18 Read 14744 rows and found 30 numeric columns 17:35:18 Using Annoy for neighbor search, n_neighbors = 30 17:35:18 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 17:35:20 Writing NN index file to temp file /tmp/Rtmpe9PkZW/file15eb5e1394b443 17:35:20 Searching Annoy index using 1 thread, search_k = 3000 17:35:24 Annoy recall = 100% 17:35:25 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 17:35:28 Initializing from normalized Laplacian + noise (using RSpectra) 17:35:28 Commencing optimization for 200 epochs, with 610890 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 17:35:35 Optimization finished Running SCTransform on assay: RNA Running SCTransform on layer: counts vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. Variance stabilizing transformation of count matrix of size 16028 by 5446 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 5000 cells Found 58 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 16028 genes Computing corrected count matrix for 16028 genes Calculating gene attributes Wall clock passed: Time difference of 20.37882 secs Determine variable features Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Getting residuals for block 1(of 2) for counts dataset Getting residuals for block 2(of 2) for counts dataset Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Finished calculating residuals for counts Set default assay to SCT Running SCTransform on assay: RNA Running SCTransform on layer: counts vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. Variance stabilizing transformation of count matrix of size 16759 by 9298 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 5000 cells Found 177 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 16759 genes Computing corrected count matrix for 16759 genes Calculating gene attributes Wall clock passed: Time difference of 27.36476 secs Determine variable features Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Getting residuals for block 1(of 2) for counts dataset Getting residuals for block 2(of 2) for counts dataset Centering data matrix | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Finished calculating residuals for counts Set default assay to SCT Centering and scaling data matrix | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% Centering and scaling data matrix | | | 0% | |======================= | 33% | |=============================================== | 67% | |======================================================================| 100% Finding all pairwise anchors Running CCA Merging objects Finding neighborhoods Finding anchors Found 20394 anchors Filtering anchors Retained 5732 anchors Warning: Layer counts isn't present in the assay object; returning NULL Warning: Layer counts isn't present in the assay object; returning NULL Merging dataset 1 into 2 Extracting anchors for merged samples Finding integration vectors Finding integration vector weights 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Integrating data Warning: Layer counts isn't present in the assay object; returning NULL Warning: Assay integrated changing from Assay to SCTAssay Warning: Layer counts isn't present in the assay object; returning NULL 17:42:38 UMAP embedding parameters a = 0.9922 b = 1.112 17:42:38 Read 14744 rows and found 30 numeric columns 17:42:38 Using Annoy for neighbor search, n_neighbors = 30 17:42:38 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 17:42:39 Writing NN index file to temp file /tmp/Rtmpe9PkZW/file15eb5e57496998 17:42:39 Searching Annoy index using 1 thread, search_k = 3000 17:42:44 Annoy recall = 100% 17:42:45 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 17:42:47 Initializing from normalized Laplacian + noise (using RSpectra) 17:42:48 Commencing optimization for 200 epochs, with 644944 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 17:42:54 Optimization finished Found 2 SCT models. Recorrecting SCT counts using minimum median counts: 16811.5 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 Warning messages: 1: Different cells and/or features from existing assay SCT 2: Different cells and/or features from existing assay SCT 3: Different cells and/or features from existing assay SCT 4: In createCellMarker2_GeneSets(species, tissue, minGsSize) : No cell markers found for Human: Airway epithelium