__STDOUT LOG__ Job runs on fgcz-h-906 at /scratch/o5495_o5444_ScSeurat_2024-12-05--10-03-16_Differentiated_lung_cells_temp1724580 Starting EzAppScSeurat ScSeurat2 o5495_o5444_ScSeurat_2024-12-05--10-03-16_Differentiated_lung_cells_temp1724580 2024-12-05 10:36:19 Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. Uploading data to Enrichr... Done. Querying Azimuth_Cell_Types_2021... Done. Parsing results... Done. INFO [2024-12-05 11:09:34] Skipping pathway and TF activity __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... 8 clusters Creating ~7824 artificial doublets... Dimensional reduction Evaluating kNN... Training model... iter=0, 702 cells excluded from training. iter=1, 551 cells excluded from training. iter=2, 495 cells excluded from training. Threshold found:0.382 481 (4.9%) 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 11:04:58 UMAP embedding parameters a = 0.9922 b = 1.112 11:04:58 Read 9298 rows and found 20 numeric columns 11:04:58 Using Annoy for neighbor search, n_neighbors = 30 11:04:58 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 11:04:59 Writing NN index file to temp file /tmp/RtmpCCkx42/file1a50b46e10842d 11:04:59 Searching Annoy index using 4 threads, search_k = 3000 11:05:00 Annoy recall = 100% 11:05:01 Commencing smooth kNN distance calibration using 4 threads with target n_neighbors = 30 11:05:04 Initializing from normalized Laplacian + noise (using RSpectra) 11:05:04 Commencing optimization for 500 epochs, with 378798 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 11:05:15 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 11:05:20 2024 .. Analyzing all cells Thu Dec 5 11:05:20 2024 .... Generating UMAP Thu Dec 5 11:05:52 2024 .... Estimating contamination Thu Dec 5 11:05:55 2024 ...... Completed iteration: 10 | converge: 0.03674 Thu Dec 5 11:05:59 2024 ...... Completed iteration: 20 | converge: 0.01359 Thu Dec 5 11:06:02 2024 ...... Completed iteration: 30 | converge: 0.007252 Thu Dec 5 11:06:05 2024 ...... Completed iteration: 40 | converge: 0.004852 Thu Dec 5 11:06:08 2024 ...... Completed iteration: 50 | converge: 0.003361 Thu Dec 5 11:06:11 2024 ...... Completed iteration: 60 | converge: 0.002508 Thu Dec 5 11:06:14 2024 ...... Completed iteration: 70 | converge: 0.001944 Thu Dec 5 11:06:18 2024 ...... Completed iteration: 80 | converge: 0.00169 Thu Dec 5 11:06:21 2024 ...... Completed iteration: 90 | converge: 0.001221 Thu Dec 5 11:06:24 2024 ...... Completed iteration: 99 | converge: 0.0009892 Thu Dec 5 11:06:24 2024 .. Calculating final decontaminated matrix -------------------------------------------------- Completed DecontX. Total time: 1.128765 mins -------------------------------------------------- 10 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 230 genes passed tf-idf cut-off and 18 soup quantile filter. Taking the top 18. Using 54 independent estimates of rho. Estimated global rho of 0.03 Expanding counts from 7 clusters to 9298 cells. Calculating cluster 0 Calculating cluster 1 Calculating cluster 2 Calculating cluster 3 Calculating cluster 4 Calculating cluster 5 Calculating cluster 6 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): Alveolar pneumocyte Type II, Anti-tumor immune cell, Conventional dendritic cell 1(cDC1), Conventional dendritic cell 2(cDC2), Effector T(Teff) cell, Exhausted CD8+ T cell Warning: Overwriting miscellanous data for model 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 Normalizing query using reference SCT model Warning: 696 features of the features specified were not present in both the reference query assays. Continuing with remaining 2304 features. Projecting cell embeddings Finding query neighbors Finding neighborhoods Finding anchors Found 1718 anchors Finding integration vectors Finding integration vector weights 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Predicting cell labels Predicting cell labels Predicting cell labels Predicting cell labels Predicting cell labels Predicting cell labels Integrating dataset 2 with reference dataset Finding integration vectors Integrating data Computing nearest neighbors Running UMAP projection 11:14:32 Read 9298 rows 11:14:32 Processing block 1 of 1 11:14:32 Commencing smooth kNN distance calibration using 4 threads with target n_neighbors = 20 11:14:32 Initializing by weighted average of neighbor coordinates using 4 threads 11:14:32 Commencing optimization for 67 epochs, with 185960 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 11:14:33 Finished Projecting reference PCA onto query Finding integration vector weights 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Projecting back the query cells into original PCA space Finding integration vector weights 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Computing scores: Finding neighbors of original query cells Finding neighbors of transformed query cells Computing query SNN Determining bandwidth and computing transition probabilities 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Total elapsed time: 4.72664451599121 /var/spool/slurmd/job11608/slurm_script: line 96: 1724596 Killed R --vanilla --slave <