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] "LIHCHs1" 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 13626 by 1661 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 1661 cells | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Found 75 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 13626 genes | | | 0% | |== | 4% | |===== | 7% | |======== | 11% | |========== | 14% | |============ | 18% | |=============== | 21% | |================== | 25% | |==================== | 29% | |====================== | 32% | |========================= | 36% | |============================ | 39% | |============================== | 43% | |================================ | 46% | |=================================== | 50% | |====================================== | 54% | |======================================== | 57% | |========================================== | 61% | |============================================= | 64% | |================================================ | 68% | |================================================== | 71% | |==================================================== | 75% | |======================================================= | 79% | |========================================================== | 82% | |============================================================ | 86% | |============================================================== | 89% | |================================================================= | 93% | |==================================================================== | 96% | |======================================================================| 100% Computing corrected count matrix for 13626 genes | | | 0% | |== | 4% | |===== | 7% | |======== | 11% | |========== | 14% | |============ | 18% | |=============== | 21% | |================== | 25% | |==================== | 29% | |====================== | 32% | |========================= | 36% | |============================ | 39% | |============================== | 43% | |================================ | 46% | |=================================== | 50% | |====================================== | 54% | |======================================== | 57% | |========================================== | 61% | |============================================= | 64% | |================================================ | 68% | |================================================== | 71% | |==================================================== | 75% | |======================================================= | 79% | |========================================================== | 82% | |============================================================ | 86% | |============================================================== | 89% | |================================================================= | 93% | |==================================================================== | 96% | |======================================================================| 100% Calculating gene attributes Wall clock passed: Time difference of 1.000838 mins Determine variable features Set 3000 variable features Place corrected count matrix in counts slot Centering data matrix | | | 0% | |==== | 5% | |======= | 11% | |=========== | 16% | |=============== | 21% | |================== | 26% | |====================== | 32% | |========================== | 37% | |============================= | 42% | |================================= | 47% | |===================================== | 53% | |========================================= | 58% | |============================================ | 63% | |================================================ | 68% | |==================================================== | 74% | |======================================================= | 79% | |=========================================================== | 84% | |=============================================================== | 89% | |================================================================== | 95% | |======================================================================| 100% Set default assay to SCT There were 50 or more warnings (use warnings() to see the first 50) PC_ 1 Positive: IGKC, IGHG1, IGHG3, TMSB10, CD74, TMSB4X, IGHG4, IGHGP, IGFBP7, IGLC3 CCL21, IGLC2, TIMP1, HLA-DRA, HLA-B, HLA-DRB1, PTGDS, HAMP, CCL19, IL32 S100A6, CD52, HLA-A, IGHM, TAGLN, HLA-DPA1, AEBP1, B2M, CXCR4, HLA-DPB1 Negative: GLUL, REG3A, DCXR, MT1G, KNG1, APOE, APOC3, AZGP1, HSD11B1, MT1X TF, PLG, CTH, AGT, EPHX1, SCD, CYP2E1, MT2A, AHSG, ADH1B TAT, G6PC, CYP3A4, SPARCL1, PAH, HGD, AQP9, ZFAND5, AADAC, SLC38A3 PC_ 2 Positive: IGKC, IGHG1, IGHG3, IGHGP, IGLC3, IGHG4, IGLC2, IGHM, REG3A, MT1G MT1X, GLUL, PTGDS, MT2A, DCXR, SPARCL1, IGLL5, CTH, S100A6, HSD11B1 AZGP1, JCHAIN, APOE, MZB1, AGT, MME, KNG1, RHBG, ZFP36L2, AHSG Negative: ALB, HAMP, FABP1, FGB, FGA, SPINK1, ORM1, SERPINA1, HP, FGG A2M, SDS, CYP2A6, IL32, CYP3A5, ANG, C9, UGT2B7, FGL1, HLA-B IGFBP3, IFI27, F9, B2M, GPX3, ORM2, PCK1, FTCD, HLA-A, PLA2G2A PC_ 3 Positive: CCL21, CCL19, CD74, TMSB4X, TMSB10, TIMP1, CXCR4, IGFBP7, CD52, PTGDS CYR61, TXNIP, MS4A1, CORO1A, JAK3, VIM, ARHGAP45, TMC8, CD53, HLA-DRA ARHGEF1, EVL, LAPTM5, LIMD2, IL7R, S100A6, TRBC2, HLA-DQB1, TBC1D10C, COL6A2 Negative: IGHG1, IGHG3, IGKC, IGLC3, IGHGP, IGHG4, IGLC2, APOC1, HP, FABP1 IGLL5, HAMP, FGG, ORM1, FGA, SERPINA1, FGB, ALB, JCHAIN, ANG ORM2, SSR4, IGHM, ADIRF, CYP3A4, SPINK1, CES1, MZB1, A2M, CYP2E1 PC_ 4 Positive: ALB, SDS, HAMP, IGHG4, CYP2A6, AD000090.1, GPX3, CYP4A11, NEAT1, IGKC F9, PCK1, UGT2B7, CYP3A5, EPHX1, MLXIPL, FTCD, ACSL4, IGHG3, ERRFI1 TAT, AFM, PTGDS, MAT1A, FOSB, CCL21, A2M, DEFB1, C9, IGHGP Negative: ORM1, SAA1, MT2A, FTH1, TMSB10, REG3A, IL32, ORM2, PLA2G2A, MT1G MT1X, APOC1, TPT1, ADIRF, IFI27, TMSB4X, HLA-B, B2M, SOD2, TOMM7 CXCL10, LCN2, SAA2, CYP2E1, HP, HLA-DRA, HPR, APOC2, HLA-C, GLUL PC_ 5 Positive: CYP3A4, ADIRF, FTH1, GLUL, IFI27, MALAT1, APOC1, HLA-DRA, C1QA, C1QB UGT2B4, HLA-DPA1, TOMM7, HLA-DRB1, EPHX1, FTCD, IGFBP3, AKR1B10, CYP2A6, TPT1 HLA-C, UGT2B15, CYP2E1, APOC2, ALDH1L1, CD74, TMSB10, IGHM, C1QC, CES1 Negative: REG3A, SAA1, HP, LCN2, FGB, SAA2, A2M, FGG, SERPINA1, SPINK1 FGA, CHI3L1, FGL1, MT1G, REG1A, MT1X, ALB, PLA2G2A, CCL21, MT2A NNMT, ACSL4, LYZ, ORM1, CCL19, IGKC, SPP1, PIGR, IGHG3, DEFB1 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 1661 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/Rtmps0pJ2e/file553a66970a22d 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 66938 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: 1661 Number of edges: 60390 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.7165 Number of communities: 8 Elapsed time: 0 seconds [1] 3000 1661 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 4 4 4 7 5 10 8 10 11 12 14 13 14 16 14 13 16 19 19 21 22 [1] "10" $m_ALB [1] "ALB" "HAMP" "SDS" "FGB" "FABP1" [6] "A2M" "CYP2A6" "FGA" "HP" "FGG" [11] "SPINK1" "SERPINA1" "ACSL4" "C9" "CYP3A5" [16] "AD000090.1" "GPX3" "F9" "DEFB1" "CYP4A11" [21] "CRP" "FOS" "ANG" "UGT2B7" "SERPINE1" [26] "FGL1" "NNMT" "DNAJB1" "CP" $m_IGKC [1] "IGKC" "IGHG1" "IGHG3" "IGLC3" "IGHGP" "IGHG4" [7] "IGLC2" "TMSB10" "CCL21" "IGFBP7" "TMSB4X" "CD74" [13] "TIMP1" "PTGDS" "CCL19" "IGHM" "S100A6" "TAGLN" [19] "IGLL5" "AEBP1" "CD52" "CXCR4" "HLA-DRB1" "LAPTM5" [25] "CYR61" "LIMD2" "SH3BGRL3" $m_ORM1 [1] "ORM1" "CXCL9" "ADIRF" "IFI27" "IL32" "HLA-B" "B2M" [8] "CXCL10" "HLA-DRA" "FTH1" "C1QB" "HLA-C" "HLA-A" "PLA2G2A" [15] "C1QC" $m_WWP2 [1] "WWP2" "GLI4" "MPHOSPH10" "SAV1" "BCL2L12" "FRMD4A" [7] "BLZF1" "BANP" $m_LINC00844 [1] "LINC00844" "BEX3" "GSTM1" "CYP2E1" "GLUL" [6] "AHSG" "ADH1B" "PAGE4" "APOC1" "LINC01419" [11] "PLG" "TF" "HSD11B1" "PLVAP" "RTN4RL2" [16] "G6PC" "CD36" "SPARCL1" "DCXR" "TDGF1" [21] "AC104958.2" "KNG1" "AKR1C2" $m_MT1G [1] "MT1G" "MT1X" "MT2A" "MT1E" "GCLC" [6] "NEK4" "SULT4A1" "AGT" "FAM8A1" "CLCN3" [11] "AC018647.2" "SLC16A11" "XPNPEP2" "IRS2" "SNX3" [16] "PPP1R3G" "GLUD1" "RBP7" "SEZ6L2" $m_DENND5B [1] "DENND5B" "TAB3" "SEC14L2" "AC020656.1" "SLC23A2" [6] "SLC22A3" "NME7" "TIMM23B" "SH3BP5L" "SLC6A12" [11] "IGSF8" "FADS2" "TTC1" "PAPPA2" "FAM160A2" [16] "ZDHHC5" "NRCAM" "SMUG1" "TGDS" "ACOX2" $m_BMP1 [1] "BMP1" "SULT1B1" "FYB2" "NR4A2" "NRIP1" "NKD1" "NAGS" [8] "TBC1D24" "HNF1A" "ATN1" "ANKRD9" "INTS3" "ZXDC" "DHTKD1" [15] "UQCC1" "PCK1" "WDFY1" "GOLIM4" "ST3GAL1" "ATE1" "SNX33" [22] "AMPD2" "ECM2" "VSNL1" "LRRC58" "TEF" "MLXIPL" "EHHADH" [29] "HIP1R" $m_REG3A [1] "REG3A" "LCN2" "SAA1" "SAA2" "REG1A" "TDP2" "COBLL1" [8] "ASPSCR1" "KLK4" "LYZ" "PROX1" "ARL4A" "FUBP3" "SPP1" [15] "ATP1B1" "PIGR" "IGF2R" "ICK" "GOPC" "COL7A1" $m_NUS1 [1] "NUS1" "LBX2-AS1" "CPM" "MYBBP1A" "CEP164" "DECR2" [7] "ACSL6" "CLDN14" "SETD7" "SLC38A4" "VPS51" "PAOX" 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_ALB Calculating cluster m_IGKC Calculating cluster m_ORM1 Calculating cluster m_WWP2 Calculating cluster m_LINC00844 Calculating cluster m_MT1G Calculating cluster m_DENND5B Calculating cluster m_BMP1 Calculating cluster m_REG3A Calculating cluster m_NUS1 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. null device 1