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] "BRCAHs2" 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 15692 by 2346 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 2346 cells | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Found 82 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 15692 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 15692 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: CD24, SCGB1D2, IGFBP5, H3F3A, AGR2, PPIA, SH3BGRL, PIP, DYNLL1, DUSP4 MYL12B, ARF1, SRP9, H2AFZ, COX6C, DSTN, ZFP36L2, BAMBI, HSPA8, HINT1 TFF1, TOP2A, EEF1A1, PTMA, LINC00052, CRABP2, MGP, AGR3, CSTB, SCGB2A1 Negative: IGKC, C3, DCN, AEBP1, COL1A1, SERPING1, C1R, FTL, LRP1, CD74 TAGLN, BGN, IGLC3, IGLC2, IGHG3, APOD, CCDC80, IGHA1, TIMP1, FOS SFRP2, COL1A2, MMP2, FBLN1, ID3, HLA-A, FBLN2, LUM, MFAP4, COL3A1 PC_ 2 Positive: IGKC, IGHG3, APOD, IGLC3, IGHA1, IGLC2, JCHAIN, FOS, MFAP4, IGHG2 MZB1, PI16, SERPING1, C3, GPX3, FABP4, TNXB, CCL19, C1R, IGHG4 IGHM, IGHG1, CCL21, DEPP1, CCDC80, ACKR1, NR4A1, GSN, FBLN1, MGP Negative: FN1, COL1A2, COL1A1, HLA-DRA, POSTN, CD74, HLA-DPA1, COL3A1, LUM, FTL HLA-DPB1, APOC1, HLA-DRB1, TYROBP, SPARC, HLA-B, CXCL14, HLA-DQB1, AEBP1, FTH1 C1QB, HLA-DQA1, MMP11, COL6A3, COL5A2, CTSD, TIMP3, COL12A1, COL11A1, B2M PC_ 3 Positive: TFF3, AZGP1, MDK, TFF1, KRT19, ERBB2, IGFBP4, MUC1, CTSD, PYDC1 CLDN4, NUPR1, FXYD3, IGFBP5, RABAC1, HLA-A, SPDEF, OBP2B, DHCR7, IGFBP2 KRT8, STC2, ARAP1, KRT7, S100A6, GAA, GIPC1, GPC1, BPIFB1, FMOD Negative: FN1, COL1A2, COL3A1, COL1A1, POSTN, SPARC, LUM, ACTA2, COL6A3, CTGF COL4A1, COL5A2, H3F3A, SH3BGRL, ADAMTS1, SFRP2, FBN1, COL6A1, IGFBP7, THBS2 EEF1A1, A2M, DCN, TMSB4X, VIM, MYL12B, B2M, VCAN, TIMP3, TPM1 PC_ 4 Positive: CD74, HLA-DRA, B2M, HLA-DPA1, HLA-B, HLA-DPB1, HLA-DRB1, TMSB4X, HLA-A, EEF1A1 TPT1, CCL5, TRBC2, HLA-DQA1, HLA-DQB1, JCHAIN, CXCL9, CD52, IGHA1, IL32 CXCL10, C1QB, PSMB9, TRBC1, LYZ, TRAC, CCL19, RAC2, HLA-C, TYROBP Negative: COL1A1, COL1A2, FN1, AEBP1, SFRP4, WISP2, CTGF, LUM, LRP1, POSTN FBLN1, COL3A1, COL6A1, TNXB, DCN, SFRP2, CLU, COL6A3, TIMP1, BGN COMP, KRT19, IGFBP4, CCDC80, PODN, TAGLN, MXRA8, MGP, COL5A1, PI16 PC_ 5 Positive: C3, IGLC2, IGHA1, IGLC3, DCN, FBLN1, C1R, MGP, MFAP4, IGKC CCDC80, LRP1, APOD, C1S, IGHM, IGLC7, PI16, LUM, SFRP4, IGHG1 SERPING1, CD74, CCL19, SERPINF1, WISP2, IGHG4, CCL5, JCHAIN, COL1A2, MMP2 Negative: COL4A1, RGS5, COL4A2, A2M, VWF, NDUFA4L2, MCAM, IGFBP7, SPARCL1, GNG11 EGFL7, PLVAP, CAV1, ACTA2, PECAM1, COL18A1, IGFBP3, FABP4, AQP1, CD93 SPARC, PODXL, GJA4, ENG, CDH5, ECSCR, EPAS1, ESAM, JAG1, CD34 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 10:10:50 UMAP embedding parameters a = 0.9922 b = 1.112 10:10:50 Read 2346 rows and found 10 numeric columns 10:10:50 Using Annoy for neighbor search, n_neighbors = 30 10:10:50 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:10:51 Writing NN index file to temp file /tmp/RtmpKdbPTQ/file4b0dc2bed28a9 10:10:51 Searching Annoy index using 1 thread, search_k = 3000 10:10:52 Annoy recall = 100% 10:10:52 Commencing smooth kNN distance calibration using 1 thread 10:10:54 Initializing from normalized Laplacian + noise 10:10:54 Commencing optimization for 500 epochs, with 93784 positive edges 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 10:11:05 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: 2346 Number of edges: 74005 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.7953 Number of communities: 10 Elapsed time: 0 seconds [1] 3000 2346 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 5 6 7 6 8 9 10 11 9 8 12 13 13 16 14 15 13 15 18 19 21 [1] "7" $m_IGFBP5 [1] "IGFBP5" "TFF1" "PIP" "H3F3A" "CD24" "SCGB1D2" [7] "AGR2" "SH3BGRL" "PPIA" "DYNLL1" "SPP1" "MYL12B" [13] "ADAMTS1" "MGP" "H2AFZ" "BAMBI" "DUSP4" "TOP2A" [19] "SRP9" "LINC00052" "ARF1" "COX6C" "DSTN" "HSPA8" [25] "ZFP36L2" "HINT1" "STC2" "AGR3" "SHISA2" "PTMA" [31] "MALAT1" "CEACAM6" "EEF1A1" "CSTB" "CRABP2" "SCGB2A1" [37] "CENPF" "TUBA1B" "SCD" "CDKN1A" "LRRC75A" $m_TFF3 [1] "TFF3" "KRT19" "AZGP1" "MDK" "ERBB2" "CLDN4" [7] "TMEM132A" "MUC1" "OBP2B" "GPC1" "PYDC1" "KRT7" [13] "IGFBP2" "GIPC1" "SPDEF" "PGGHG" "GAA" "ARAP1" [19] "DHCR7" "PRSS8" "RHPN1" "KIF12" "SH3GLB2" "RABAC1" [25] "SLC25A39" "NDUFB7" "SREBF1" "NPDC1" "CD151" "DPP7" [31] "KRT8" "TSPAN4" "NUPR1" "STARD3" "TMEM205" "ELOB" [37] "HID1" "CHRD" "TRMT1" "ADAM15" "TRPM4" "SCAP" [43] "FXYD3" "TMEM141" "NME3" "CTSD" "NDUFS8" "GUK1" [49] "BRAT1" "EPHB3" "PTOV1" "ST14" "CHPF" "NAGLU" [55] "MAZ" "EPS8L2" "NECTIN2" "CCS" "SLC38A10" "NOXA1" [61] "TECR" "TNK2" "PLXNB1" "ILVBL" "CTSF" "S100A13" [67] "FMOD" "ITGA3" "PIGQ" "CCDC130" "TRIM28" "DDR1" [73] "RHOT2" "TCIRG1" "MICALL2" "TMEM63A" "ARL6IP4" "EIF6" [79] "GSDMD" "PFKL" "FAAP100" "C19orf53" "FIS1" $m_C3 [1] "C3" "LRP1" "SERPING1" "FBLN1" "C1R" "TIMP1" [7] "WISP2" "TNXB" "CCDC80" "DCN" "FOS" "PODN" [13] "AEBP1" "BGN" "GSN" "MXRA8" "IGFBP4" "GPX3" $m_COL4A1 [1] "COL4A1" "COL4A2" "VWF" "A2M" "NDUFA4L2" "GNG11" [7] "MCAM" "RGS5" "PLVAP" "SPARCL1" "EGFL7" "PECAM1" [13] "IGFBP7" "AQP1" "CAV1" "ACTA2" "CD34" "COL15A1" [19] "ESAM" "CD93" "GJA4" "LBH" "LAMA4" "IGFBP3" [25] "PODXL" "HSPG2" "PLXDC1" "RBP7" "EPAS1" "STOM" [31] "ENG" "CDH5" "COL18A1" "OLFML2A" "ECSCR" "LAMC1" [37] "PLPP1" "RAMP3" "RAMP2" "NRP1" "VIM" "ICAM2" [43] "CSPG4" $m_IGKC [1] "IGKC" "IGHG3" "IGHA1" "IGLC2" "JCHAIN" "IGHG2" "IGLC3" "MZB1" [9] "CCL5" "IGHG1" "IGHM" "TRBC2" "CD52" "TRAC" $m_CD74 [1] "CD74" "HLA-DRA" "HLA-DPA1" "HLA-DPB1" "HLA-DRB1" "FTL" [7] "HLA-B" "HLA-DQB1" "TYROBP" "C1QB" "ISG15" "HLA-DQA1" [13] "APOC1" "B2M" "MS4A6A" "FTH1" "HLA-A" "CXCL10" [19] "AIF1" "MX1" "HLA-DRB5" "C1QA" "HLA-DMA" "LYZ" [25] "HLA-C" "FCGR3A" "TREM2" "LST1" "C1QC" "TPT1" [31] "TAP1" "TMSB4X" "LAPTM5" "STAT1" "CXCL14" "FYB1" [37] "HCLS1" "NPC2" "SLCO2B1" "RARRES3" "COTL1" "PSMB9" [43] "ITGB2" "IFIT1" "HLA-DOA" "CD68" "CSF1R" "FGL2" [49] "RASSF4" "PSMB8" "OAS1" "CXCL9" "IFIT3" "IRF1" [55] "SLC15A3" "SAT1" "MARCKS" $m_COL1A1 [1] "COL1A1" "FN1" "COL1A2" "COL3A1" "POSTN" "LUM" "SPARC" [8] "COL6A3" "TIMP3" "COL5A2" "CTGF" "CTSK" "COL5A1" "COL12A1" [15] "FBN1" "THBS2" "SFRP2" "CTHRC1" "VCAN" "COL11A1" "COL10A1" [22] "COL8A1" "PLAU" "CDH11" "MMP11" "MFAP5" 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 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. Calculating cluster m_IGFBP5 Calculating cluster m_TFF3 Calculating cluster m_C3 Calculating cluster m_COL4A1 Calculating cluster m_IGKC Calculating cluster m_CD74 Calculating cluster m_COL1A1 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. null device 1