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] "PDACHs2" 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 15415 by 1855 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 1855 cells | | | 0% | |================== | 25% | |=================================== | 50% | |==================================================== | 75% | |======================================================================| 100% Found 70 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 15415 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 15415 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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TFF2, SPINK1, TFF1, CRISP3 REG1A, AGR2, TFF3, ANXA4, MUC5B, TCN1, FCGBP, TM4SF4, TSPAN8, PGC LCN2, GPX2, KRT8, LGALS4, ATP1B1, SLPI, MT1G, KRT19, ANPEP, SERPINA1 Negative: ACTG2, TAGLN, DES, TPM2, ACTA2, MYH11, MYL9, GREM1, CNN1, FLNA MYLK, CSRP1, IGFBP7, LGALS1, MGP, TPM1, COL3A1, SYNPO2, ACTB, COL1A2 SMOC2, CALD1, C1S, FLNC, SPARC, TUBA1A, PLN, COL1A1, SMTN, CRYAB PC_ 2 Positive: PRSS2, PRSS1, CTRB2, CEL, CELA3A, CTRB1, CPA1, CLPS, CELA2A, CPB1 PGC, CELA3B, CTRC, SYCN, MUC6, CPA2, PNLIP, PLA2G1B, MUC5AC, GP2 PDIA2, MT1G, S100P, TFF1, PNLIPRP1, TFF2, MSMB, AMY2A, CELA2B, AC139749.1 Negative: CRISP3, SCGB3A1, TFF3, TCN1, LGALS2, ANXA4, TM4SF4, FCGBP, CES1, FXYD2 MUC5B, PIGR, LCN2, FTH1, DEFB1, HLA-DPA1, GPX2, DMBT1, SLPI, ANPEP CEACAM6, GC, MMP7, SERPINA1, C6, GABRP, SLC4A4, LGALS4, FGGY, FOLR1 PC_ 3 Positive: IGLC2, IGKC, IGHA1, IGLC3, JCHAIN, IGHG3, CCL19, IGHG1, CCL21, IGHM C3, CLDN5, EGFL7, ACKR1, PTGDS, ADIRF, IGHGP, MZB1, VWF, VIM TNXB, CD52, IFITM2, C1QA, IL32, TYMP, CXCL9, MMP2, ENG, CCL5 Negative: ACTG2, MYH11, TPM2, DES, AC020656.1, GREM1, CNN1, TPM1, MYLK, TAGLN LYZ, ACTA2, CD24, AGR2, MYL9, ANXA4, TFF1, TSPAN8, CRISP3, DSTN SYNPO2, ATP1B1, CYSTM1, PIGR, CSRP1, TM4SF4, SPINK1, CLDN10, SMOC2, PLN PC_ 4 Positive: TFF3, SCGB3A1, GHRL, FXYD2, MSLN, LGALS2, PPY, MUC5B, MYL9, INS ADIRF, RARRES2, IGLC2, REG1A, TAGLN, CES1, LGALS1, DES, PPP1R14A, IGLC3 CRISP3, TPPP3, FOLR1, FLNA, RAMP1, CTRB1, PTGDS, TCN1, CELA3A, CEL Negative: PGC, MT1G, CXCL9, TFF1, MMP1, MUC6, CXCL10, MUC5AC, S100P, LYZ WARS, CLDN18, MSMB, CTSE, ANXA10, MLPH, VMP1, GBP1, MT1X, TRNP1 FABP5, CXCL17, SAT1, SRGN, MT1E, ACTB, S100A9, COL3A1, CAPN8, CCL5 PC_ 5 Positive: MUC6, PGC, TFF2, ADIRF, TFF1, MT1G, MMP1, EGFL7, S100P, MUC5AC MT1E, CLDN18, CLDN5, AC139749.1, LTBP4, IGFBP7, CTSE, MLPH, MT1M, ACKR1 CAPN8, FAM3D, ERN2, LYZ, VWF, TRNP1, KCNN4, ANXA10, TM4SF1, INS Negative: CXCL10, CXCL9, CTRB1, REG1A, CEL, CELA3A, PRSS1, CTRB2, PRSS2, CELA3B CELA2A, CLPS, CTRC, CPA1, SYCN, CPB1, CRISP3, CPA2, GABRP, GBP1 HLA-DPA1, WARS, IDO1, TCN1, FTH1, PNLIP, ANXA4, SERPINA1, TM4SF4, HLA-DQA1 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 11:02:10 UMAP embedding parameters a = 0.9922 b = 1.112 11:02:10 Read 1855 rows and found 10 numeric columns 11:02:10 Using Annoy for neighbor search, n_neighbors = 30 11:02:10 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 11:02:10 Writing NN index file to temp file /tmp/RtmppBot2Y/file223174a470359 11:02:10 Searching Annoy index using 1 thread, search_k = 3000 11:02:11 Annoy recall = 100% 11:02:12 Commencing smooth kNN distance calibration using 1 thread 11:02:13 Initializing from normalized Laplacian + noise 11:02:13 Commencing optimization for 500 epochs, with 73396 positive edges 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 11:02:22 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: 1855 Number of edges: 58569 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.8151 Number of communities: 11 Elapsed time: 0 seconds [1] 3000 1855 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 5 6 7 8 9 10 11 10 11 12 14 12 13 11 17 19 16 16 21 15 16 [1] "11" $m_DES [1] "DES" "ACTG2" "TAGLN" "TPM2" "MYL9" "MYH11" [7] "ACTA2" "GREM1" "CNN1" "FLNA" "CSRP1" "MYLK" [13] "TPM1" "SYNPO2" "CALD1" "SMOC2" "FLNC" "LGALS1" [19] "CRYAB" "DSTN" "SMTN" "FHL1" "PLN" "RGS5" [25] "TUBA1A" "SYNM" "SELENOM" "CHRDL2" "WFDC1" "LMOD1" [31] "PDLIM3" "KCNMB1" "PPP1R12B" "ACTB" "CARMN" "HSPB7" [37] "THBS1" "TNC" "MXRA7" "RGMA" "ACTN1" "PDLIM7" [43] "PCP4" "C1S" "HSPB6" "SCRG1" $m_CD52 [1] "CD52" "CXCL9" "ARHGDIB" "CCL5" "TRBC2" "LCP1" "HCST" [8] "FCER1G" "ITGB2" "GBP1" "TYROBP" "S100A8" "S100A9" "AIF1" [15] "COTL1" "TRBC1" $m_C3 [1] "C3" "MMP2" "COL1A1" "COL3A1" "COL1A2" "LUM" [7] "COL6A1" "TNXB" "SFRP2" "SERPINF1" "DCN" "COL6A3" [13] "MGP" "THY1" "FBLN1" "CTSK" "ELN" "COL5A1" [19] "FN1" "SPARC" "COL5A2" "SPON2" "PLA2G2A" "C1R" [25] "COL6A2" "TIMP1" "CCDC80" "COL14A1" "PCOLCE" "C7" [31] "ISLR" "TIMP3" "BGN" "TPSAB1" "S100A4" "CXCL12" [37] "GAS7" "IGFBP4" $m_TFF3 [1] "TFF3" "SCGB3A1" "PPY" "GHRL" "RARRES2" "LTBP4" [7] "MUC5B" "INS" "MSLN" "FXYD2" "PCSK1N" "RAMP1" [13] "SMIM22" "TPPP3" "ADAMTSL2" $m_VWF [1] "VWF" "ACKR1" "PECAM1" "A2M" "CLDN5" "ID3" [7] "PLVAP" "RAMP2" "ID1" "SLCO2A1" "DEPP1" "EGFL7" [13] "ADIRF" "NOTCH3" "EPAS1" "ECSCR" "VIM" "SOX18" [19] "RAMP3" "CLEC14A" "GNG11" "IGFBP3" "PDGFRB" "CAV1" [25] "SNCG" "IFITM2" "COL18A1" "AQP1" "PODXL" "ESAM" [31] "HYAL2" "CD93" "IGFBP7" "MMRN2" "CALCRL" "NDUFA4L2" [37] "ROBO4" "SHANK3" "C11orf96" "ENG" "SPARCL1" "FAM167B" [43] "CD34" "FAM110D" "IL3RA" "ENPP2" "SELP" "SOCS3" [49] "CPE" "LRRC32" $m_IGKC [1] "IGKC" "IGLC2" "IGHA1" "IGHG3" "IGLC3" "JCHAIN" [7] "IGHG1" "IGHM" "MZB1" "IGHGP" "CCL19" "ACAP1" [13] "CCL21" "IGHG4" "TBC1D10C" "IGLC7" "MAP4K1" "MYO1F" [19] "REG3A" "ARHGAP4" "DEF6" $m_CRISP3 [1] "CRISP3" "REG1A" "TCN1" "TM4SF4" "ANXA4" [6] "AC020656.1" "SLPI" "C6" "ANPEP" "LGALS2" [11] "CES1" "SLC4A4" "CYB5A" "DMBT1" "CEACAM6" [16] "GC" "DEFB1" "FGGY" "CLDN10" "PAH" [21] "TSPAN8" "APCS" "C12orf75" $m_FOS [1] "FOS" "MAVS" "PLCG2" "EGR1" "INPP5E" [6] "NFATC4" "GNL1" "TRIM66" "MAFK" "LAMA5" [11] "DENND3" "ANKRD27" "FAM241A" "EFCC1" "TLE4" [16] "HID1" "TNFRSF10B" "DUSP1" "SPG7" "GTF2H1" [21] "AL354836.1" "ASAP3" "TMEM107" $m_FCGBP [1] "FCGBP" "SERPINA1" "GABRP" "TFF2" "CXCL11" "LTF" "IDO1" $m_MUC6 [1] "MUC6" "PGC" "TFF1" "MT1G" "MMP1" [6] "LYZ" "MUC5AC" "S100P" "MLPH" "CAPN8" [11] "MT1E" "CTSE" "CLDN18" "TRNP1" "AC139749.1" [16] "MT1X" $m_PRSS2 [1] "PRSS2" "CTRB1" "PRSS1" "CEL" "CTRB2" "CPA1" [7] "CELA3A" "CLPS" "CPB1" "CELA2A" "CTRC" "CELA3B" [13] "SYCN" "PNLIP" "PLA2G1B" "CPA2" "GP2" "PDIA2" [19] "PNLIPRP1" 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_DES Calculating cluster m_CD52 Calculating cluster m_C3 Calculating cluster m_TFF3 Calculating cluster m_VWF Calculating cluster m_IGKC Calculating cluster m_CRISP3 Calculating cluster m_FCGBP Calculating cluster m_MUC6 Calculating cluster m_PRSS2 Warning message: CombinePlots is being deprecated. Plots should now be combined using the patchwork system. null device 1