__STDOUT LOG__ Job runs on fgcz-h-901 at /scratch/o5495_o5444_ScSeuratCombine_2024-12-06--16-23-35_temp1905051 Starting EzAppScSeuratCombine SCReportMultipleSamplesSeurat o5495_o5444_ScSeuratCombine_2024-12-06--16-23-35_temp1905051 2024-12-06 16:23:44 [1] 1 [1] 2 __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 26.47173 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 31.42528 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 16:28:59 UMAP embedding parameters a = 0.9922 b = 1.112 16:28:59 Read 14744 rows and found 30 numeric columns 16:28:59 Using Annoy for neighbor search, n_neighbors = 30 16:28:59 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 16:29:01 Writing NN index file to temp file /tmp/RtmpzofK0w/file1d11b413072962 16:29:01 Searching Annoy index using 1 thread, search_k = 3000 16:29:07 Annoy recall = 100% 16:29:08 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 16:29:12 Initializing from normalized Laplacian + noise (using RSpectra) 16:29:12 Commencing optimization for 200 epochs, with 610890 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 16:29:19 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 22.78392 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 30.98981 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% PC_ 1 Positive: FTH1, SAT1, TPT1, SERF2, CD24, NDRG1, C4orf3, CLDN1, CCND2, HIF1A MXD4, SH3BGRL3, SELENOW, RPL39, PERP, CTSB, GUK1, BRI3, TUBA1A, SHC1 TMSB4X, ATP5F1E, RPS28, OST4, ALOX15B, KLF6, PLOD2, ERO1A, RPL37A, FOLR3 Negative: PTMA, TUBA1B, HMGB1, H2AZ1, TUBB4B, HSP90AA1, NPM1, NASP, HMGN2, STMN1 DEK, RRM2, RANBP1, BIRC5, EIF4A1, PA2G4, SNRPB, TUBA1C, HSPD1, SNRPD1 PRMT1, PTTG1, NCL, HSP90AB1, HMGA1, CDC20, SERBP1, MCM7, FGFBP1, LDHB PC_ 2 Positive: TANC2, EHBP1, ADK, EXOC4, THSD4, EXOC6B, TRIO, SMYD3, EXT1, PTPRK HMGA2, LPP, PARD3, ARHGEF28, RASAL2, PSD3, EIF4G3, PTPRG, JMJD1C, RBMS3 SBF2, PATJ, RAD51B, FRMD6, MICAL2, FRMD4B, MACF1, ENSG00000284906, EGFR, ERC1 Negative: RPS27A, RPL8, GAPDH, LDHA, RPL32, RPS23, RPS12, RPL29, RPL26, RPLP1 RPL28, RPL41, MIF, RPS28, DYNLL1, RPS14, RPS3, RPL12, RPL39, RPL34 RPL10, RPL13, RPS18, MT1X, RPS3A, UBB, RPS19, GSTP1, HSPA8, LCN2 PC_ 3 Positive: TOP2A, PLK1, UBE2C, CENPF, GTSE1, MKI67, AURKA, TPX2, ASPM, RPL41 UBE2S, HES4, HMMR, TACC3, AURKB, ARL6IP1, CENPE, KIF2C, CCNF, CDCA8 CENPA, CCNA2, CDK1, FAM83D, HES2, NOTCH3, CAMK2N1, KIF23, NDC80, KIF14 Negative: KRT19, ANXA2, MYL6, S100A2, S100A11, MGST3, G0S2, EMP3, NDUFA13, MT-RNR2 IFITM3, PRDX1, MYL12B, BLVRB, GSTP1, CAVIN3, KRT5, MT-CO3, PKM, PFN1 MT-ATP6, PSMB6, CD63, GSTO1, IFITM2, ATP5MC3, CLIC1, TIMP1, PRDX5, MT2A PC_ 4 Positive: MT2A, G0S2, MT1E, ITGB1, SPHK1, PLOD2, NT5E, IL1A, LAMC2, TUBB RAI14, PRR11, TGFBR2, ACTG1, SMURF2, CYGB, CXCL1, CAVIN1, LTBP2, AREG FN1, ICAM1, C12orf75, DKK3, AXL, LAMB3, RPL41, MAP1B, PTGS2, CSF3 Negative: SPRR1B, KRT16, KRT14, PPL, KRT17, SLPI, S100A9, ADIRF, RHCG, S100A8 TGM1, SPRR2D, KRT19, HSPB1, KLK10, S100A6, AQP3, PI3, A2ML1, NCCRP1 GSTP1, SULT2B1, SPRR2A, KLK5, TMEM45B, KRT6A, S100A11, S100A14, CRABP2, NOTCH3 PC_ 5 Positive: S100A11, LGALS1, GSTP1, MYL6, BST2, TNNT1, ANXA2, ACTB, S100A10, UBE2C PRDX5, FAM83D, AURKA, HSPB1, SFN, KRT19, PFN1, PLK1, TXNDC17, PKM GSTM3, VIM, TOP2A, KIF23, TMSB10, PIF1, HSPA1A, SGO2, CKS2, KPNA2 Negative: SLC16A3, KRT7, CDH13, TPM2, ME1, GPX3, LRMDA, LAPTM4B, PRXL2A, FARP1 RAB32, FTL, F12, IRAK3, C3orf14, CDH11, FKBP10, LDAH, CITED4, CRIP2 CXCL1, MCM3, POLA1, SLBP, ASAP1, WDR76, ANGPTL4, MYO5C, SPAG16, PRKCA PC_ 1 Positive: S100A2, LGALS1, IGFBP7, COL17A1, ITGB4, FRMD6, MT2A, TINAGL1, FHOD3, ITGA3 FLNA, TENM2, CALD1, VIM, PSAP, PMEPA1, FBXO32, FAP, LAMC2, ITGB6 CTSH, CAV1, CD44, IGFBP2, SFN, ACTN1, PTHLH, MIF, KRT6A, FN1 Negative: SLC6A14, MUC4, WFDC2, S100P, CXCL17, CSTB, MUC20, ELF3, DHRS9, LCN2 AGR2, MUC1, RDH10, INSR, SAT1, VMO1, RARRES1, CLDN7, ATP1B1, IDO1 CEACAM6, B4GALT5, MYEOV, GOLM1, PRSS22, FAM83A, PSCA, PLEKHG7, TMC5, SLC44A4 PC_ 2 Positive: S100A9, GAPDH, PRDX2, KRT19, ID1, SERPINA3, NDUFA13, S100A8, CD63, S100A11 IFITM3, LDHB, SLPI, PRDX1, H3-3A, UBB, EIF4A1, MT1X, PPIB, RPL8 SAA1, PRDX5, TIMP1, CTSC, CTSD, NPC2, SAA2, CYBA, HSD17B2, PSMB1 Negative: TRIO, MGAM, KLF6, LPP, PTPRK, HDAC9, INPP4B, SAMD4A, TPM4, MICAL2 UBE2H, INHBA, VCL, MAP1B, MYO1E, MYH9, RND3, ARHGEF28, RABGAP1L, MAN2A1 MACF1, NAV2, DSG2, PHACTR4, THSD4, IFFO2, PALLD, PALM2AKAP2, TANC2, CCN2 PC_ 3 Positive: TMSB10, CD24, SELENOW, PFN1, SERF2, ATP5F1E, INHBA, COL1A1, CLTB, MGAM TAGLN, ACTB, FOLR3, KLF6, JPT1, SERPINE1, DYNLL1, IL32, C4orf48, S100A10 GUK1, ATP5MK, EIF4EBP1, SPHK1, SH3BGRL3, ADAMTS6, FSTL3, TPM1, OCIAD2, MYL6 Negative: ANK3, DPYD, CPD, ID1, NFIA, PBX1, MECOM, EFNA5, KIAA1217, PTCHD4 EYA2, GMDS, CYP1B1, AIG1, AKR1C2, MEIS2, ADD3, S100A9, SEMA4B, HSD17B2 LRMDA, CAMK1D, CP, TNS3, TSHZ2, EXOC4, PDE4D, MFSD1, GLI3, ARHGAP24 PC_ 4 Positive: RPS28, RPL39, RPS12, RPL41, RPL34, RPS8, RPL10, MT-ND3, RPL28, RPL12 RPS27A, MT-CO2, TPT1, RPS23, RPS14, RPL26, RPLP1, RNF145, RPS3, EEF1A1 FTL, RPL23, RPS18, RPL32, RPL37A, FTH1, TOMM7, GPX2, RPL13, SELENOW Negative: MYL6, UBC, KRT19, ANXA2, KRT17, CD63, TUBB4B, PRDX1, NDUFA13, EIF4A1 MYL12B, TUBA1B, KRT18, PRDX2, KRT8, SERPINB6, CYBA, CLIC1, PSMB3, TUBA1C PSMB6, EIF6, HSP90AB1, PPIB, PSMB1, S100A11, TUBA1A, IER3, PSMD8, HSP90AA1 PC_ 5 Positive: RHCG, CDA, CTSC, MT1X, S100A4, HES2, SPRR1B, ABCA1, FTH1, PI3 IL1B, RPL41, SERPINB4, S100A8, S100A6, SPRR2A, KYNU, RPL34, KRT16, RPL39 SPRR2D, CXCL2, RAB31, IL1R1, S100A10, TOMM7, PGLYRP4, CXCL8, CSF3, SERPINB13 Negative: MT-RNR2, MT-ATP6, MT-ND4, MT-RNR1, HLA-A, HLA-C, MT-ND1, RAMP1, MT-CO1, MT-CO3 MT-CYB, KRT8, KRT7, TUBB4B, MT-ND5, TPM2, GRN, B2M, TPM4, UBC TUBA1B, MT-CO2, KRT18, MT-ND6, HLA-B, CTSD, SLC16A3, ASRGL1, ACTB, HSP90AA1 Computing within dataset neighborhoods Finding all pairwise anchors Projecting new data onto SVD Projecting new data onto SVD Finding neighborhoods Finding anchors Found 7277 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 16:34:20 UMAP embedding parameters a = 0.9922 b = 1.112 16:34:20 Read 14744 rows and found 30 numeric columns 16:34:20 Using Annoy for neighbor search, n_neighbors = 30 16:34:20 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 16:34:22 Writing NN index file to temp file /tmp/RtmpzofK0w/file1d11b46e1dc8e1 16:34:22 Searching Annoy index using 1 thread, search_k = 3000 16:34:28 Annoy recall = 100% 16:34:29 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 16:34:33 Initializing from normalized Laplacian + noise (using RSpectra) 16:34:33 Commencing optimization for 200 epochs, with 626248 positive edges Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 16:34:40 Optimization finished Found 2 SCT models. Recorrecting SCT counts using minimum median counts: 16811.5 Calculating cluster 0 /var/spool/slurmd/job11918/slurm_script: line 67: 1905076 Killed R --vanilla --slave <