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] "OVCAHs2" 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 15588 by 3110 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 3110 cells | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Found 69 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 15588 genes | | | 0% | |== | 3% | |==== | 6% | |======= | 9% | |========= | 12% | |=========== | 16% | |============= | 19% | |=============== | 22% | |================== | 25% | |==================== | 28% | |====================== | 31% | |======================== | 34% | |========================== | 38% | |============================ | 41% | |=============================== 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|==================================================================== | 97% | |======================================================================| 100% Computing corrected count matrix for 15588 genes | | | 0% | |== | 3% | |==== | 6% | |======= | 9% | |========= | 12% | |=========== | 16% | |============= | 19% | |=============== | 22% | |================== | 25% | |==================== | 28% | |====================== | 31% | |======================== | 34% | |========================== | 38% | |============================ | 41% | |=============================== | 44% | |================================= | 47% | |=================================== | 50% | |===================================== | 53% | |======================================= | 56% | |========================================== | 59% | |============================================ | 62% | |============================================== | 66% | |================================================ | 69% | 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PC_ 1 Positive: TAGLN, TIMP1, TMSB10, MYL9, ACTA2, PTGDS, MGP, ACTG2, AEBP1, RBP1 FTL, IGFBP4, SFRP4, MT2A, TPM2, HTRA1, MYH11, STAR, C1R, TPT1 IGFBP2, SERPINF1, MYLK, NBL1, FTH1, S100A6, SELENOM, CEBPD, TPM1, DES Negative: SCGB2A1, LCN2, WFDC2, MUC5B, MMP7, PIGR, LTF, SLPI, MUC1, CD24 SCGB2A2, CD74, CST1, MUC6, SLC34A2, TACSTD2, SERPINA1, MSLN, FXYD3, SAT1 KRT7, CP, DEFB1, ELF3, UCA1, PDZK1IP1, MSX1, ACSL5, S100A7, GPX2 PC_ 2 Positive: DLK1, STAR, MT2A, ATP1B3, RBP1, ACTG2, CTGF, TAGLN, TPM1, TNFRSF12A CMSS1, MEG3, ECEL1, CRISPLD2, ACTA2, LXN, SCG2, HOPX, FTH1, IGKC MARCKSL1, TMSB10, DIRAS3, MYLK, RARRES1, CLU, IGHG3, IGFBP4, GUK1, NSG1 Negative: VWF, A2M, CLDN5, TIMP3, EGFL7, ENPP2, ADIRF, GPX3, CXCL12, FOS C3, IFI27, HLA-B, AQP1, DEPP1, TM4SF1, EPAS1, CLEC14A, COL3A1, ESAM COL1A1, ADGRF5, GJA5, SPARCL1, CD34, SPARC, NDUFA4L2, RAMP2, MMRN2, DCN PC_ 3 Positive: C3, PTGDS, AEBP1, SFRP4, S100A6, RARRES2, C1R, CST3, TIMP1, CPXM2 DCN, HTRA1, INMT, CXCL12, TNXB, TAGLN, FTL, OGN, BGN, SERPING1 SERPINE2, MFAP4, MYL9, C1S, C7, EMILIN1, ITGA11, SPARC, THBS1, CFD Negative: DLK1, MT2A, A2M, IGKC, CTGF, IGLC2, STAR, IGHG3, CLDN5, IGHG1 ATP1B3, IGHGP, IGFBP3, TIMP3, VWF, ENPP2, MEG3, IGLC3, DDIT4, COL18A1 CLEC14A, CRISPLD2, SAT1, EPAS1, SCG2, GJA5, EGFL7, RAMP2, RGS5, B2M PC_ 4 Positive: IGKC, IGLC2, COL1A1, IGLC3, IGHA1, MALAT1, IGHG3, COL1A2, IGHG1, IGHGP AD000090.1, CTGF, JCHAIN, SPARC, COL3A1, SCGB1D4, AEBP1, COL16A1, IGHG4, SCGB2A1 MEG3, IGHG2, COL6A1, XIST, CDHR1, THBS1, COL5A1, NEAT1, ISLR, BGN Negative: FTH1, TPT1, MT2A, TAGLN, NACA, FTL, STAR, TMSB4X, RBP1, ATP1B3 TMSB10, A2M, IFI27, PTMA, ACTG2, B2M, CD74, JUNB, SRGN, IGFBP3 DEPP1, MGP, ENPP2, TIMP3, CMSS1, GUK1, CLDN5, CEBPD, ACTA2, S100A4 PC_ 5 Positive: CTGF, MEG3, MALAT1, AD000090.1, COL1A2, MMP7, COL6A1, XIST, ENC1, CDHR1 SFRP1, CADPS, COL6A3, YAP1, CALD1, F3, GOLGA8B, MACF1, DDX17, KLHDC8A VCAN, FSTL1, CYR61, GOLGA8A, THBS1, SLC38A2, LCN2, COL4A1, PLAT, SOX4 Negative: IGKC, IGLC2, IGHGP, IGHG1, IGLC3, IGHG3, IGHA1, JCHAIN, FTL, FTH1 TPT1, CD74, MGP, IGHG2, IGHG4, TAGLN, TIMP1, MZB1, HLA-DRB1, CST3 PTGDS, HLA-DRA, NACA, TMSB10, RARRES2, S100A6, HLA-DPA1, PPP1R14A, C3, SFRP4 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 10:11:22 UMAP embedding parameters a = 0.9922 b = 1.112 10:11:22 Read 3110 rows and found 10 numeric columns 10:11:22 Using Annoy for neighbor search, n_neighbors = 30 10:11:22 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:11:23 Writing NN index file to temp file /tmp/Rtmppy0RlG/file446959f69918 10:11:23 Searching Annoy index using 1 thread, search_k = 3000 10:11:25 Annoy recall = 100% 10:11:26 Commencing smooth kNN distance calibration using 1 thread 10:11:27 Initializing from normalized Laplacian + noise 10:11:27 Commencing optimization for 500 epochs, with 124210 positive edges 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:11:42 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: 3110 Number of edges: 101248 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.7839 Number of communities: 11 Elapsed time: 0 seconds [1] 3000 3110 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 5 4 5 7 8 9 10 11 11 12 11 12 14 13 17 15 20 19 19 19 20 [1] "5" $m_C3 [1] "C3" "PTGDS" "SFRP4" "AEBP1" "RARRES2" "TIMP1" [7] "DCN" "C7" "CPXM2" "C1R" "INMT" "FTL" [13] "S100A6" "TNXB" "CXCL12" "CST3" "MGP" "OGN" [19] "ITGA11" "HTRA1" "SERPINE2" "C1S" "MFAP4" "SERPING1" [25] "CCDC80" "EFEMP1" "PLA2G2A" "COL14A1" "BGN" "THBS1" [31] "CFD" "S100A10" "LOXL1" "PCOLCE" "LUM" "SPARC" [37] "COL11A1" "EMILIN1" "IGF1" "PDGFRA" "NTRK2" "LPP" [43] "PLAT" "SERPINF1" "C1QB" "THBS4" "MYH11" "PPP1R14A" [49] "LTBP2" "MYL9" "C1QA" "TPM2" $m_MT2A [1] "MT2A" "STAR" "DLK1" "ATP1B3" "RBP1" "ACTG2" [7] "FTH1" "JUNB" "TNFRSF12A" "GUK1" "TAGLN" "LXN" [13] "TPM1" "CMSS1" "HOPX" "SCG2" "ACTA2" "TMSB4X" [19] "ECEL1" "DIRAS3" "C11orf96" "CREM" "SERPINE1" "NACA" [25] "MARCKSL1" "RARRES1" "TMSB10" "CRISPLD2" "ADAMTS4" "RGS2" [31] "PGAP1" "NSG1" "DES" "NBL1" "MYLK" "TPT1" $m_AD000090.1 [1] "AD000090.1" "IGKC" "IGLC2" "MALAT1" "CTGF" [6] "IGLC3" "MEG3" "IGHG3" "IGHG1" "IGHGP" [11] "XIST" "COL1A1" "COL1A2" "CDHR1" "IGHA1" [16] "IGHG4" "TCF4" "VCAN" "KLHDC8A" "CYR61" [21] "ENC1" "IGFBP5" "COL6A3" "COL5A1" "COL6A1" [26] "MACF1" "SYNPO" "COLEC11" "JCHAIN" "CCNL2" [31] "IGHG2" "CALD1" "DDX5" "HSPG2" "ISLR" [36] "YAP1" "COL5A2" "VMP1" "FSTL1" "DDX17" [41] "ACTN4" $m_VWF [1] "VWF" "A2M" "CLDN5" "TIMP3" "IGFBP3" "EGFL7" [7] "EPAS1" "AQP1" "TM4SF1" "ENPP2" "DEPP1" "HLA-B" [13] "ADIRF" "CD34" "IFI27" "COL4A1" "JAG1" "S100A4" [19] "RAMP2" "NOTCH3" "FOS" "B2M" "PRSS23" "ESAM" [25] "RGS5" "PECAM1" "TGFBR2" "CD93" "SPARCL1" "LMO2" [31] "ZFP36" "COL18A1" "CLEC14A" "TXNIP" "EMP1" "CDH5" [37] "SRGN" "CALCRL" "GNG11" "COL4A2" "ACKR1" "MMRN2" [43] "PTP4A3" "ELK3" "NDUFA4L2" "ELN" "GJA5" "HBB" [49] "GPX3" "IL33" "LDB2" "ICAM2" "PTPRB" "PLXND1" [55] "SLC9A3R2" "SLIT3" "PDGFRB" "DUSP1" "RAMP3" "ADGRL4" [61] "TFPI" "ADGRF5" "ADAMTS1" "ECSCR" "RBP7" "GJA4" [67] "FBLN2" "PDK4" "TSC22D1" "NID1" "RHOB" "KLF2" [73] "DUSP6" "PLPP1" "CXorf36" "SOD3" "LAMA5" "ADAM15" [79] "NOTCH4" "MT1E" "AHNAK" "TSC22D3" "SPTBN1" "MFGE8" [85] "ADH1B" "RGCC" "PHLDA1" "TBX2" "EDN1" "SSFA2" [91] "BTG1" "HOXD9" "PEAR1" "EFNA1" "SLCO2A1" "PXN" [97] "ROBO4" "FHL1" "HIGD1B" "EFNB2" "COX7A1" "HYAL2" [103] "SPRY1" "CXCL2" "FLT1" "CD9" $m_MMP7 [1] "MMP7" "SCGB2A1" "LCN2" "WFDC2" "MUC5B" "CD74" [7] "PIGR" "SLPI" "MUC1" "LTF" "HLA-DRA" "SCGB2A2" [13] "CD24" "SAT1" "NEAT1" "CTSB" "CST1" "SERPINA1" [19] "SCGB1D4" "ACSL5" "SPINT2" "KRT8" "TNFAIP2" "APCDD1" [25] "TACSTD2" "MUC6" "KRT19" "S100A9" "C12orf75" "MSX1" [31] "RNASET2" "XBP1" "PCCA" "SLC34A2" "SCGB1D2" 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. null device 1 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 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. Calculating cluster m_C3 Calculating cluster m_MT2A Calculating cluster m_AD000090.1 Calculating cluster m_VWF Calculating cluster m_MMP7 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. null device 1