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] "PDACHs1" 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 15181 by 1789 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 1789 cells | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Found 59 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 15181 genes | | | 0% | |== | 3% | |===== | 6% | |======= | 10% | |========= | 13% | |=========== | 16% | |============== | 19% | |================ | 23% | |================== | 26% | |==================== | 29% | |======================= | 32% | |========================= | 35% | |=========================== | 39% | |============================= | 42% | 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|==================================================================== | 97% | |======================================================================| 100% Computing corrected count matrix for 15181 genes | | | 0% | |== | 3% | |===== | 6% | |======= | 10% | |========= | 13% | |=========== | 16% | |============== | 19% | |================ | 23% | |================== | 26% | |==================== | 29% | |======================= | 32% | |========================= | 35% | |=========================== | 39% | |============================= | 42% | |================================ | 45% | |================================== | 48% | |==================================== | 52% | |====================================== | 55% | |========================================= | 58% | |=========================================== | 61% | |============================================= | 65% | |=============================================== | 68% | |================================================== | 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COL1A2, TIMP1, MT2A, COL3A1, SPARC FN1, AEBP1, COL18A1, MYL9, MGP, RARRES2, ACTA2, BGN, VIM, ADIRF LUM, COL4A1, COL4A2, C1R, A2M, MCAM, COL6A2, EMILIN1, FLNA, LGALS1 Negative: TM4SF1, LGALS3, LYZ, SPINK1, B2M, ANXA2, CEACAM6, SPRR3, S100A10, CD24 KRT17, S100P, GABRP, TSPAN8, FTH1, CD55, PTMA, MYL12B, S100A14, ERO1A EIF4A2, AGR2, EPCAM, KRT8, CYSTM1, KRT18, TXN, SPRR1B, C15orf48, SERPINB1 PC_ 2 Positive: ADIRF, TIMP1, C11orf96, CD74, MT2A, MCAM, PPP1R14A, MGP, HLA-DRB1, TGM2 IGFBP4, ADAMTS1, PI15, HLA-DRA, PTP4A3, GPX3, MT1M, COL18A1, IGKC, GADD45B MT1X, NDUFA4L2, SPARCL1, CLU, MT1E, SOD3, C1QA, C1QB, CLDN5, HLA-DPB1 Negative: COL3A1, COL1A2, COL1A1, FN1, LUM, RARRES2, SPARC, COL6A3, THBS2, COL12A1 COL5A2, MMP2, SFRP2, POSTN, COL11A1, COL5A1, TGFBI, MMP11, LOXL2, COL6A1 TIMP3, GREM1, COL6A2, CTHRC1, CCDC80, DCN, COL10A1, SPP1, CTSK, CTGF PC_ 3 Positive: LUM, LYZ, COL1A2, IGFBP7, MMP11, ARL14, SPARC, SFRP2, DMBT1, MMP7 CD24, TXN, COL3A1, ACTA2, COL12A1, COL1A1, GABRP, PPIA, TSPAN8, COL4A1 HTRA1, POSTN, AQP5, ASPN, GPX2, HSPE1, CTSC, S100P, CTHRC1, OLFM4 Negative: MT2A, FTH1, NDRG1, SERPINE1, SPP1, FTL, VEGFA, ANGPTL4, BGN, SLC2A1 SPRR1B, GAPDH, ERO1A, ENO1, SLC2A3, SPRR3, TIMP3, CTSD, TGFBI, FN1 TIMP1, C15orf48, IGFBP3, MME, ENO2, SLC6A8, NUPR1, NMB, APOE, ITGA5 PC_ 4 Positive: VIM, FTH1, TPT1, EEF1A1, FN1, B2M, TMSB4X, LGALS3, IGFBP3, ERO1A IGFBP7, LDHA, MGP, SPARCL1, ZFAS1, SPRR3, ADAMTS1, EPAS1, SAT1, NDRG1 MCAM, PTMA, A2M, GNG11, SRGN, SPARC, PPIA, MALAT1, RGS5, COL3A1 Negative: S100A6, CTSD, KRT19, RARRES2, HTRA3, IFI27, KRT8, KRT17, MUC1, CLDN4 EMILIN1, TYMP, NBL1, MXRA8, KRT7, COMP, APOE, ARFGAP1, NNMT, THY1 AEBP1, ISLR, FLNA, KRT18, LY6E, IGHG4, SPHK1, IGKC, IER3, MRC2 PC_ 5 Positive: COL4A1, PLVAP, VWF, FLT1, COL4A2, EGFL7, CD93, PECAM1, COL18A1, ESM1 SEMA3F, STC1, IGFBP3, CDH5, CCL18, ESAM, NOTCH4, GNG11, HSPG2, ECSCR BCL6B, IGKC, CLEC14A, MGP, NID1, PLXND1, IGHA1, CXCR4, TIE1, TNFRSF4 Negative: TAGLN, MT2A, ADIRF, MYL9, ACTA2, MT1M, MT1E, MT1X, MYH11, GADD45B FN1, IGFBP4, CLU, SOD3, C11orf96, MFAP4, PPP1R14A, C1QTNF1, ACTG2, CEBPD SPEG, COL11A1, SELENOM, ITIH3, DEPP1, S100A4, TPM2, CSRP1, LYZ, IFITM2 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 10:10:32 UMAP embedding parameters a = 0.9922 b = 1.112 10:10:32 Read 1789 rows and found 10 numeric columns 10:10:32 Using Annoy for neighbor search, n_neighbors = 30 10:10:32 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:10:33 Writing NN index file to temp file /tmp/RtmpBstOYb/file4d9ff4f73d5f9 10:10:33 Searching Annoy index using 1 thread, search_k = 3000 10:10:33 Annoy recall = 100% 10:10:34 Commencing smooth kNN distance calibration using 1 thread 10:10:35 Initializing from normalized Laplacian + noise 10:10:36 Commencing optimization for 500 epochs, with 72112 positive edges 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:10:44 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: 1789 Number of edges: 60660 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.7569 Number of communities: 8 Elapsed time: 0 seconds [1] 3000 1789 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 5 5 7 8 8 10 10 12 11 11 13 14 14 14 17 16 18 19 20 21 23 [1] "12" $m_LYZ [1] "LYZ" "ARL14" "CD24" "MMP7" "GABRP" "TXN" [7] "TSPAN8" "FOS" "S100P" "SERPINA1" "AGR2" "CCL20" [13] "DMBT1" "HSPE1" "TOB1" "PPIA" "MYL12B" "CTSC" [19] "ID1" "SLC20A1" "LCN2" "DKK1" "S100A14" "OLFM4" [25] "CD55" "TCN1" "SERPINB1" "SERPINB3" "AQP5" "KLF5" [31] "IER3" "HSP90AA1" "CYSTM1" "SLC6A14" "GPX2" "ANXA2" [37] "PTMA" "PTTG1" "TCIM" "ITGA6" "ITGB6" $m_PRSS2 [1] "PRSS2" "AHDC1" "CNNM2" "ABCF2.1" "SLC39A3" [6] "AL139246.5" "ZBTB8A" "CEP112" "UBN1" "TPST2" [11] "TPSAB1" "PAQR7" "SBF1" "YJEFN3" "TLR2" [16] "ACAP1" "NPL" "CTBP2" "PCED1B" "PRDM8" [21] "ERMARD" "SLC25A37" $m_SPRR3 [1] "SPRR3" "C15orf48" "SPRR1B" "ERO1A" "LGALS3" [6] "CEACAM5" "FTH1" "SPINK1" "TM4SF1" "CEACAM6" [11] "COL17A1" "ITGA2" "LAMC2" "B2M" "EIF4A2" [16] "APOL1" "EPCAM" "AL121761.2" "DUOX2" "F3" [21] "TFF2" "S100A10" "TACSTD2" "SPNS2" "PLAUR" [26] "RNASET2" "LMO7" "PTGS2" "DHRS9" "CLIC3" [31] "SAT1" "SPRR2A" "PLAT" "MALAT1" "EPS8L1" [36] "LAMB3" "RAB11FIP1" $m_XPC [1] "XPC" "ZBED5" "ABL2" "HSPA6" "TUT4" [6] "AC025159.1" "DIAPH2" "AL590617.2" "KLHL17" "GGA1" [11] "MED19" "DHRS1" "PILRB" "ZNF557" "ZFYVE16" [16] "PTPRU" "SCAF4" "TRMT44" "NFKBIZ" "RNF169" [21] "SPTAN1" "GGT7" "COG1" "APOD" "RBM6" [26] "DEGS1" "PRR14L" "WDR74" "ZBTB17" "ATP13A1" [31] "SDHAF1" "SIPA1L2" "ICAM1" "C17orf107" "KIAA0100" [36] "LENG8" "PDPK1" "ZNF213-AS1" "SARM1" "SLC27A5" [41] "PHC1" "JMJD6" "BTN2A1" "CNTROB" "GDI1" [46] "IKZF5" "ACSF2" "AFF1" "BOD1L1" $m_COL3A1 [1] "COL3A1" "COL1A2" "LUM" "COL1A1" "SPARC" "SFRP2" [7] "MMP11" "COL12A1" "POSTN" "MMP2" "COL11A1" "CTHRC1" [13] "DCN" "RARRES2" "COL6A3" "COL5A2" "ITGA11" "C1S" [19] "HTRA1" "THBS2" "FNDC1" "VCAN" "CTSK" "FBLN1" [25] "ACTA2" "ANTXR1" "ASPN" "COL10A1" "SERPINF1" "CD99" [31] "AEBP1" "COL5A1" "OLFML3" "CCDC80" $m_ISG15 [1] "ISG15" "IFIT3" "RSAD2" "IFIT1" "MX1" "IFIT2" "LTB4R" "ZNF446" [9] "OASL" "CXCL10" "OAS1" "POLR3A" "MX2" "DCAF5" "PAN2" "AHRR" [17] "CLEC2D" "KAT8" $m_SPP1 [1] "SPP1" "NDRG1" "PLIN2" "VEGFA" "FTL" "MME" "SLC2A3" "CXCL8" [9] "IGFBP3" $m_CYR61 [1] "CYR61" "CTGF" "BGN" "SERPINE1" "THBS1" "SLC11A1" "ITGA5" [8] "ACTB" $m_S100A6 [1] "S100A6" "IFI27" "CLDN4" "MUC1" "KRT19" "ST14" "KRT8" "KRT17" [9] "KRT7" "MSLN" "KRT18" "IGFBP2" "BLVRB" "LRFN3" "NBEAL2" "LY6E" $m_C11orf96 [1] "C11orf96" "TIMP1" "ADIRF" "IGFBP4" "MT2A" "HLA-DRB1" [7] "TGM2" "PPP1R14A" "CD74" "TAGLN" "SOD3" "MGP" [13] "GADD45B" "MT1E" "CLU" "PI15" "GPX3" "MT1X" [19] "MCAM" "IGFBP7" "ADAMTS1" "HLA-DRA" "MT1M" "MYL9" [25] "ACTG2" "C1QA" "MYH11" "PTP4A3" "SPEG" "ENG" [31] "NDUFA4L2" "SELENOM" "COL18A1" "C1QB" "HLA-DPB1" $m_COL4A1 [1] "COL4A1" "VWF" "PLVAP" "PECAM1" "CD93" "COL4A2" "GNG11" "FLT1" [9] "EGFL7" "ESM1" "STC1" $m_IGKC [1] "IGKC" "IGHG4" "IGLC2" "CCL18" "IGHG3" "IGHG1" 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 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. Calculating cluster m_LYZ Calculating cluster m_SPRR3 Calculating cluster m_COL3A1 Calculating cluster m_SPP1 Calculating cluster m_CYR61 Calculating cluster m_S100A6 Calculating cluster m_C11orf96 Calculating cluster m_COL4A1 Calculating cluster m_IGKC Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. null device 1