This functions allows you to Preprocess, Cluster and Reduce Dimensions for a single seurat object.
seurat_pipeline(
seu,
assay = "gene",
resolution = 0.6,
reduction = "pca",
organism = "human",
...
)A Seurat object
Assay of interest in Seurat object
Resolution for clustering cells. Default set to 0.6.
Dimensional reduction object seu
Organism
Extra parameters passed to seurat_pipeline
processed_seu <- seurat_pipeline(panc8)
#> Centering and scaling data matrix
#> PC_ 1
#> Positive: CD99, ERO1B, HMGN2, AC124312.1, MT-ND6, MT-ND1, HACD3, MT-ATP6, AL035071.1, NORAD
#> ATP5F1A, RACK1, PRUNE2, FAM171B, PRELID3B, EFNA5, AL354740.1, ARFGEF3, TENT5C, SARAF
#> G6PC2, PCSK1, ATP5PB, PFKFB2, ATP5F1C, ATP5F1B, UNC79, RGPD5, HNRNPA1, SLC25A6
#> Negative: IFITM3, RHOC, LGALS3, ZFP36L1, SERPING1, LITAF, TACSTD2, SERPINA3, KRT7, LCN2
#> SDC4, PRSS8, DHRS3, CFB, CTSH, ANXA4, CLDN1, COL18A1, TNFRSF12A, IL32
#> SERINC2, KRT19, TM4SF1, KRT18, SERPINH1, PDZK1IP1, CLDN4, C3, GPRC5B, PMEPA1
#> PC_ 2
#> Positive: C10orf10, CRYBA2, KRT8, VGF, CLDN4, SERPINA1, CRH, LOXL4, ATP1A1, HSPB1
#> RASD1, GATM, SERINC2, SLC44A4, KRT18, CD74, CAMK2G, GPX2, UCP2, MUC1
#> GSTA1, AQP3, PRSS8, ARRDC4, LCN2, SPINK1, S100A6, SDC4, CFB, REG1A
#> Negative: COL6A3, SPARC, F2R, NID1, COL4A1, COL15A1, TIMP3, COL1A2, PXDN, PLAT
#> COL3A1, COL1A1, BGN, COL5A1, CDH11, XAF1, MMP2, SFRP2, PDGFRB, LAMA4
#> COL5A2, VCAN, ITGA1, ADAMTS4, LRRC32, GPNMB, LUM, CRISPLD2, SRPX2, RPL10
#> PC_ 3
#> Positive: YWHAZ, OCLN, SAT1, SERPINA3, SDC4, LCN2, SOD2, TACSTD2, TM4SF1, KRT7
#> CFB, CLDN1, CPM, PDZK1IP1, C3, IL32, PRSS8, MT-ND1, MT-ATP6, RACK1
#> RPL10, TMC5, MT-ND6, CD44, ANXA4, CLDN10, PIGR, HNRNPA1, ANXA3, AKR1C3
#> Negative: TIMP1, PDGFRB, COL3A1, COL6A2, BGN, COL1A2, COL5A1, CRYBA2, SPON2, CYGB
#> SFRP2, AEBP1, MXRA8, COL15A1, MRC2, MMP2, LAMC3, THBS2, THY1, COL1A1
#> NID1, C11orf96, COL6A1, CDH11, SPARC, HTRA3, LRRC32, VCAN, LUM, IGFBP4
#> PC_ 4
#> Positive: MCAM, TINAGL1, FLT1, TM4SF18, PLVAP, ERG, IGFBP7, CFTR, MYCT1, ECSCR
#> PMEPA1, ACVRL1, MMP7, STC1, KRT23, RGCC, VTCN1, S1PR1, PCDH12, AQP1
#> IFI27, CA2, CLEC14A, EMCN, CD93, CDH5, ESAM, PTPRB, CXCR4, SPP1
#> Negative: CTRB1, CTRB2, CTRC, CPA2, PNLIP, REG1B, PRSS1, PLA2G1B, PRSS3, CPA1
#> CELA2A, KLK1, CELA3A, CPB1, PRSS3P2, PNLIPRP1, BCAT1, PNLIPRP2, RARRES2, MGST1
#> DPEP1, SYCN, ALB, CELA3B, REG3A, CXCL17, PDIA2, SPINK1, GSTA2, CEL
#> PC_ 5
#> Positive: PODXL, FLT1, ERG, PLVAP, MYCT1, ECSCR, CD93, S1PR1, EMCN, CDH5
#> CLEC14A, PCDH12, RGCC, VWF, PTPRB, PRDM1, ESAM, ACVRL1, CALCRL, F2RL3
#> ROBO4, BCL6B, TM4SF18, CXCR4, ELTD1, ICAM2, PECAM1, ESM1, CLIC2, CXorf36
#> Negative: CFTR, MMP7, ALDH1A3, IER3, SPP1, VTCN1, C1S, KRT23, SERPING1, TSPAN8
#> KRT19, TFPI2, HSD17B2, AQP1, LGALS4, LITAF, CLDN10, SLC3A1, NOTCH3, ANXA3
#> SERPINA5, CHI3L1, DEFB1, COL1A1, VCAM1, CDH6, CEACAM7, PDLIM3, SDC1, ATP1A1
#> Warning: Command RunPCA.gene changing from SeuratCommand to SeuratCommand
#> Warning: Command RunTSNE changing from SeuratCommand to SeuratCommand
#> Warning: The following arguments are not used: check_duplicates
#> 13:36:12 UMAP embedding parameters a = 0.9922 b = 1.112
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by 'spam'
#> 13:36:12 Read 1011 rows and found 30 numeric columns
#> 13:36:12 Using Annoy for neighbor search, n_neighbors = 30
#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
#> Also defined by 'spam'
#> 13:36:12 Building Annoy index with metric = cosine, n_trees = 50
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#> |
#> 13:36:12 Writing NN index file to temp file /tmp/RtmpLoWyKl/file320632545260d
#> 13:36:12 Searching Annoy index using 1 thread, search_k = 3000
#> 13:36:13 Annoy recall = 100%
#> 13:36:13 Commencing smooth kNN distance calibration using 1 thread
#> with target n_neighbors = 30
#> 13:36:15 Initializing from normalized Laplacian + noise (using RSpectra)
#> 13:36:15 Commencing optimization for 500 epochs, with 40366 positive edges
#> 13:36:16 Optimization finished
#> Warning: Command RunUMAP.gene.pca changing from SeuratCommand to SeuratCommand
#> [13:36:16] Clustering Cells...
#> Computing nearest neighbor graph
#> Computing SNN
#> Warning: Command FindNeighbors.gene.pca changing from SeuratCommand to SeuratCommand
#> clustering at 0.6 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35653
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8853
#> Number of communities: 9
#> Elapsed time: 0 seconds
#> Warning: Command FindClusters changing from SeuratCommand to SeuratCommand
#> stashing presto markers for gene_snn_res.0.2
#> stashing presto markers for gene_snn_res.0.4
#> stashing presto markers for gene_snn_res.0.6
#> stashing presto markers for gene_snn_res.0.8
#> stashing presto markers for gene_snn_res.1
#> stashing presto markers for gene_snn_res.1.2
#> stashing presto markers for gene_snn_res.1.4
#> stashing presto markers for gene_snn_res.1.6
#> stashing presto markers for gene_snn_res.1.8
#> stashing presto markers for gene_snn_res.2