Error in library(TCGA2STAT) : there is no package called ‘TCGA2STAT’ In addition: Warning message: replacing previous import ‘AUCell::cbind’ by ‘SingleCellExperiment::cbind’ when loading ‘SCENIC’ [1] "BRCAHs1" Warning: The 'show_progress' argument is deprecated as of v0.3. Use 'verbosity' instead. (in sctransform::vst) Calculating cell attributes from input UMI matrix: log_umi Variance stabilizing transformation of count matrix of size 13104 by 1863 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 1863 cells | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Found 49 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 13104 genes | | | 0% | |=== | 4% | |===== | 7% | |======== | 11% | |========== | 15% | |============= | 19% | |================ | 22% | |================== | 26% | |===================== | 30% | |======================= | 33% | |========================== | 37% | |============================= | 41% | |=============================== | 44% | |================================== | 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|================================================================= | 93% | |=================================================================== | 96% | |======================================================================| 100% Calculating gene attributes Wall clock passed: Time difference of 1.094337 mins Determine variable features Set 3000 variable features Place corrected count matrix in counts slot Centering data matrix | | | 0% | |==== | 6% | |======== | 11% | |============ | 17% | |================ | 22% | |=================== | 28% | |======================= | 33% | |=========================== | 39% | |=============================== | 44% | |=================================== | 50% | |======================================= | 56% | |=========================================== | 61% | |=============================================== | 67% | |=================================================== | 72% | |====================================================== | 78% | |========================================================== | 83% | |============================================================== | 89% | |================================================================== | 94% | |======================================================================| 100% Set default assay to SCT There were 50 or more warnings (use warnings() to see the first 50) PC_ 1 Positive: COX6C, SCGB1D2, MGP, SUB1, TFF3, FTH1, AGR2, BEX1, IGFBP5, ASAH1 SCUBE2, TFF1, PYCARD, S100P, MZT2A, HIST1H1C, CST4, MIEN1, LTF, CHCHD2 FLNB, TMEM141, FAM96B, ABAT, NEDD8, ARF1, SELENOM, C7orf50, ALB, ZBTB18 Negative: COL1A1, COL1A2, COL3A1, POSTN, FN1, SPARC, LUM, COL6A2, AEBP1, COL6A1 VIM, DCN, COL6A3, CTGF, LGALS1, TIMP3, MMP2, CTHRC1, THBS2, COL5A2 SFRP2, IGFBP7, VCAN, MMP11, ACTA2, TAGLN, TMSB4X, COL5A1, MYL9, FSTL1 PC_ 2 Positive: COL1A2, COL1A1, AEBP1, LUM, MMP11, POSTN, FN1, COL3A1, TIMP3, DCN COL10A1, COL11A1, COL5A2, COL12A1, COMP, COL6A3, ISLR, THBS2, CTHRC1, SFRP2 CTSK, MXRA5, COL5A1, COL6A1, CTGF, COL6A2, MRC2, SFRP4, C1S, RARRES2 Negative: IGFBP7, A2M, COL4A1, VWF, VIM, COL4A2, PECAM1, TMSB4X, ENG, GNG11 HSPG2, AQP1, EGFL7, COL18A1, ADGRL4, PLVAP, PODXL, RNASE1, CD93, MCAM ACTA2, HLA-E, CDH5, ESAM, HLA-DRB1, SOX18, EMCN, ACKR1, TPT1, EPAS1 PC_ 3 Positive: FTL, CD74, APOE, HLA-DRB1, HLA-DRA, TFF2, LYZ, CTSD, HLA-DPB1, APOC1 PGC, HLA-DPA1, C1QA, MUC5AC, TMSB4X, PSAP, C3, GPNMB, AD000090.1, HLA-DQA1 TYROBP, HLA-DQB1, MSMB, C1QB, TFF1, IGKC, GKN2, C1QC, MUC6, MUCL3 Negative: IGFBP7, POSTN, FN1, SPARC, MMP11, COX6C, ADGRL4, AQP1, HSPG2, IGFBP3 SPARCL1, TIMP3, ACTA2, TAGLN, COL18A1, EMCN, GNG11, EGFL7, PODXL, SUB1 COL11A1, EPAS1, ACKR1, FBLN2, CDH5, ENG, S1PR1, ELK3, COL5A2, LAMC1 PC_ 4 Positive: TFF2, PGC, MUC5AC, TFF1, LYZ, MSMB, GKN2, AD000090.1, GKN1, AGR2 MUC6, IGKC, MUCL3, MALAT1, SPINK1, AKR1B10, S100P, GPX2, ANKRD30A, TSPAN8 C19orf33, BPIFB1, IGHA1, SSR4, CYSTM1, PSCA, REG1A, VSIG2, MMP1, VSIG1 Negative: FTH1, CD74, FTL, HLA-DRB1, HLA-DPA1, HLA-DRA, APOE, HLA-DPB1, APOC1, HLA-DQA1 C1QB, COX6C, TYROBP, HLA-DQB1, B2M, SCGB1D2, C3, C1QA, PSAP, GPNMB C1QC, SUB1, LUM, CTSB, DCN, TMSB4X, COL6A3, HLA-A, TRBC1, LST1 PC_ 5 Positive: APOE, MMP11, ANKRD30A, APOC1, CTSD, COL18A1, AEBP1, COL10A1, FTL, COL11A1 COL12A1, TIMP3, ISG15, MALAT1, SAT1, SOX18, MCAM, COL5A2, PTP4A3, GJA4 IGFBP3, CLDN5, IFI6, TAGLN, A2M, SPARC, COL5A3, EGFL7, SPP1, IGLC3 Negative: TMSB4X, CCDC80, AD000090.1, VIM, TPT1, S100A10, COL6A3, VCAN, VWF, DCN HTRA1, ANXA2, PTGIS, PCOLCE, SELENOP, AGR2, MFAP4, FBLN1, COL14A1, IGFBP6 PRG4, SH3BGRL3, MMP2, IGFBP5, COL1A1, LGALS1, ANPEP, MGP, FSTL1, LTBP2 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 10:10:39 UMAP embedding parameters a = 0.9922 b = 1.112 10:10:39 Read 1863 rows and found 10 numeric columns 10:10:39 Using Annoy for neighbor search, n_neighbors = 30 10:10:39 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:10:40 Writing NN index file to temp file /tmp/RtmpjtKLOq/file49bbf244c5ae7 10:10:40 Searching Annoy index using 1 thread, search_k = 3000 10:10:40 Annoy recall = 100% 10:10:41 Commencing smooth kNN distance calibration using 1 thread 10:10:43 Initializing from normalized Laplacian + noise 10:10:43 Commencing optimization for 500 epochs, with 77080 positive edges 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:10:52 Optimization finished null device 1 Computing nearest neighbor graph Computing SNN Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 1863 Number of edges: 65059 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.7141 Number of communities: 8 Elapsed time: 0 seconds [1] 3000 1863 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 4 4 5 5 5 7 8 7 8 11 10 8 9 12 12 13 13 14 11 19 17 [1] "5" NULL Error: Cannot find 'nmf' in this Seurat object In addition: Warning message: Using `as.character()` on a quosure is deprecated as of rlang 0.3.0. Please use `as_label()` or `as_name()` instead. This warning is displayed once per session. Execution halted