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### Running command:
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###   /home/biocbuild/bbs-3.12-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data Nebulosa
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* checking for file ‘Nebulosa/DESCRIPTION’ ... OK
* preparing ‘Nebulosa’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
--- re-building ‘introduction.Rmd’ using rmarkdown
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Attaching package: 'MatrixGenerics'

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    colCounts, colCummaxs, colCummins, colCumprods, colCumsums,
    colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs,
    colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats,
    colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds,
    colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads,
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    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    tapply, union, unique, unsplit, which.max, which.min

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Loading required package: IRanges
Loading required package: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

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Loading required package: dbplyr
--- finished re-building ‘introduction.Rmd’

--- re-building ‘nebulosa_seurat.Rmd’ using rmarkdown
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PC_ 1 
Positive:  FTL, LYZ, FTH1, CST3, S100A9, S100A8, TYROBP, AIF1, LST1, FCN1 
	   LGALS1, FCER1G, S100A4, LGALS2, SAT1, S100A6, TYMP, COTL1, CTSS, IFITM3 
	   CFD, HLA-DRA, PSAP, S100A11, GPX1, OAZ1, SERPINA1, GSTP1, CD68, NPC2 
Negative:  MALAT1, CCL5, RPS27A, LTB, NKG7, RPS6, RPS27, RPS3A, RPL3, IL32 
	   RPS3, RPL13A, RPS12, RPL9, RPL21, RPSA, RPS18, CD3D, RPL13, PTPRCAP 
	   CTSW, GZMA, CD3E, CST7, IL7R, RPL34, LDHB, CD7, RPS13, CXCR4 
PC_ 2 
Positive:  CD74, HLA-DRA, CD79A, HLA-DQA1, HLA-DPB1, HLA-DQB1, CD79B, TCL1A, MS4A1, HLA-DRB1 
	   HLA-DPA1, LINC00926, LTB, VPREB3, HLA-DRB5, RPL13A, RPL13, HLA-DQA2, CD37, FCER2 
	   HLA-DMA, RPS27, RPS18, RPS2, RPS5, BANK1, RPL11, HLA-DMB, RPS6, RPS12 
Negative:  NKG7, CCL5, GZMB, GNLY, GZMA, CST7, PRF1, FGFBP2, CTSW, GZMH 
	   CCL4, SPON2, FCGR3A, B2M, CLIC3, CD247, GZMM, HOPX, KLRD1, HLA-C 
	   XCL2, ACTB, AKR1C3, IGFBP7, TTC38, S1PR5, APMAP, HLA-A, SRGN, PRSS23 
PC_ 3 
Positive:  S100A8, S100A9, JUNB, RPS12, IL7R, LDHB, RPS6, CD3D, RPS3, RPL13 
	   CD3E, TPT1, NOSIP, LYZ, RPL3, RPS18, VIM, FTL, RPL11, IL32 
	   RPS27A, FOS, RPS27, RPS2, RPL34, AQP3, RGCC, MAL, S100A6, RPS3A 
Negative:  CD74, HLA-DRA, CD79A, HLA-DPB1, NKG7, HLA-DQA1, CD79B, HLA-DRB1, HLA-DQB1, HLA-DPA1 
	   TCL1A, GZMB, MS4A1, GNLY, FGFBP2, PRF1, HLA-DRB5, CST7, LINC00926, GZMA 
	   HLA-DQA2, VPREB3, CCL5, GZMH, HLA-DMA, FCER2, CD37, CCL4, CTSW, FCGR3A 
PC_ 4 
Positive:  S100A8, S100A9, LYZ, LGALS2, CD14, GPX1, MS4A6A, GSTP1, FCN1, CCL3 
	   S100A12, NKG7, FOLR3, CEBPD, GRN, CSF3R, RBP7, GNLY, BLVRB, GAPDH 
	   ID1, IL8, VCAN, NCF1, ALDH2, ASGR1, GZMB, AP1S2, PLBD1, CST7 
Negative:  FCGR3A, LST1, FCER1G, AIF1, IFITM3, MS4A7, FTH1, RHOC, IFITM2, RP11-290F20.3 
	   COTL1, TIMP1, CDKN1C, SAT1, CKB, SERPINA1, CEBPB, ACTB, HMOX1, LILRA3 
	   LRRC25, HCK, TMSB4X, PILRA, STXBP2, SPI1, ABI3, FAM110A, BID, WARS 
PC_ 5 
Positive:  GPX1, PPBP, PF4, CCL5, SDPR, NRGN, SPARC, GNG11, HIST1H2AC, RGS18 
	   CD9, CLU, GP9, TPM4, TUBB1, ITGA2B, AP001189.4, RUFY1, TAGLN2, CA2 
	   PTCRA, MPP1, TMEM40, TREML1, PGRMC1, TUBA4A, F13A1, ACRBP, MMD, GRAP2 
Negative:  FCGR3A, LST1, AIF1, IFITM3, FCER1G, GNLY, MALAT1, FTL, GZMB, IFITM2 
	   FGFBP2, NKG7, TYROBP, PRF1, FTH1, S100A4, RPS6, MS4A7, SPON2, RP11-290F20.3 
	   SERPINA1, RPS3A, S100A11, CTSS, RPL21, NACA, RPL34, RPL11, JUNB, APOBEC3A 
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
20:35:44 UMAP embedding parameters a = 0.9922 b = 1.112
20:35:44 Read 2638 rows and found 30 numeric columns
20:35:44 Using Annoy for neighbor search, n_neighbors = 30
20:35:44 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:35:45 Writing NN index file to temp file /tmp/RtmpN5ZZOb/file7ee84714c10
20:35:45 Searching Annoy index using 1 thread, search_k = 3000
20:35:46 Annoy recall = 100%
20:35:47 Commencing smooth kNN distance calibration using 1 thread
20:35:49 Initializing from normalized Laplacian + noise
20:35:49 Commencing optimization for 500 epochs, with 109930 positive edges
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:35:54 Optimization finished
Computing nearest neighbor graph
Computing SNN
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