Regress Seurat Object by Given Set of Genes
regress_by_features(seu, feature_set, set_name, regress = TRUE, ...)A seurat object
as a string
regressed_seu <- regress_by_features(panc8, feature_set = cc.genes$s.genes, set_name = "s_genes")
#> regressing seurat objects by s_genes
#> Module score stored as s_genes1
#> Regressing out s_genes1
#> Centering and scaling data matrix
#> PC_ 1
#> Positive: CD99, ERO1B, HMGN2, AC124312.1, MT-ND6, MT-ND1, MT-ATP6, HACD3, AL035071.1, NORAD
#> ATP5F1A, PRUNE2, RACK1, FAM171B, PRELID3B, EFNA5, AL354740.1, ARFGEF3, TENT5C, G6PC2
#> SARAF, PCSK1, ATP5PB, PFKFB2, ATP5F1C, ATP5F1B, UNC79, RGPD5, HNRNPA1, SLC25A6
#> Negative: IFITM3, RHOC, LGALS3, ZFP36L1, SERPING1, LITAF, TACSTD2, SERPINA3, KRT7, LCN2
#> SDC4, DHRS3, PRSS8, CTSH, CFB, ANXA4, CLDN1, COL18A1, TNFRSF12A, IL32
#> KRT19, TM4SF1, SERINC2, KRT18, SERPINH1, PDZK1IP1, CLDN4, C3, GPRC5B, PMEPA1
#> PC_ 2
#> Positive: C10orf10, CRYBA2, VGF, KRT8, CLDN4, SERPINA1, CRH, LOXL4, HSPB1, ATP1A1
#> RASD1, SERINC2, GATM, SLC44A4, KRT18, CD74, CAMK2G, GPX2, UCP2, MUC1
#> GSTA1, ARRDC4, AQP3, PRSS8, SPINK1, LCN2, S100A6, REG1A, CFB, SDC4
#> Negative: COL6A3, SPARC, F2R, NID1, COL4A1, COL15A1, TIMP3, PXDN, COL1A2, PLAT
#> COL3A1, COL1A1, BGN, COL5A1, CDH11, XAF1, MMP2, SFRP2, PDGFRB, LAMA4
#> COL5A2, VCAN, ITGA1, ADAMTS4, LRRC32, GPNMB, LUM, CRISPLD2, SRPX2, RPL10
#> PC_ 3
#> Positive: OCLN, YWHAZ, SERPINA3, SAT1, SDC4, LCN2, TACSTD2, SOD2, CFB, KRT7
#> TM4SF1, CLDN1, CPM, PDZK1IP1, C3, IL32, PRSS8, MT-ND1, MT-ATP6, RACK1
#> RPL10, MT-ND6, TMC5, CD44, ANXA4, CLDN10, PIGR, HNRNPA1, AKR1C3, ARRDC3
#> Negative: TIMP1, PDGFRB, COL3A1, BGN, COL6A2, COL1A2, COL5A1, SPON2, CYGB, SFRP2
#> AEBP1, CRYBA2, 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, MYCT1, CFTR, ECSCR
#> PMEPA1, MMP7, ACVRL1, STC1, KRT23, RGCC, VTCN1, IFI27, PCDH12, S1PR1
#> AQP1, CLEC14A, CDH5, EMCN, CA2, CD93, PTPRB, ESAM, CXCR4, SPP1
#> Negative: CTRB1, CTRB2, CTRC, CPA2, PNLIP, REG1B, PRSS1, PLA2G1B, PRSS3, CPA1
#> CELA2A, KLK1, CELA3A, PNLIPRP1, PRSS3P2, CPB1, BCAT1, PNLIPRP2, RARRES2, MGST1
#> DPEP1, SYCN, ALB, REG3A, CELA3B, CXCL17, PDIA2, SPINK1, GSTA2, CEL
#> PC_ 5
#> Positive: PODXL, FLT1, ERG, PLVAP, MYCT1, ECSCR, CD93, S1PR1, EMCN, CDH5
#> PCDH12, CLEC14A, 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, CLDN10, LITAF, SLC3A1, NOTCH3, ANXA3
#> SERPINA5, CHI3L1, DEFB1, COL1A1, VCAM1, CDH6, CEACAM7, PDLIM3, ATP1A1, C1R
#> 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
#> 13:35:31 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'
#> 13:35:31 Read 1011 rows and found 30 numeric columns
#> 13:35:31 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'
#> 13:35:31 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> *
#> |
#> 13:35:31 Writing NN index file to temp file /tmp/RtmpLoWyKl/file320632cffaf09
#> 13:35:31 Searching Annoy index using 1 thread, search_k = 3000
#> 13:35:31 Annoy recall = 100%
#> 13:35:32 Commencing smooth kNN distance calibration using 1 thread
#> with target n_neighbors = 30
#> 13:35:33 Initializing from normalized Laplacian + noise (using RSpectra)
#> 13:35:33 Commencing optimization for 500 epochs, with 40642 positive edges
#> 13:35:35 Optimization finished
#> Warning: Command RunUMAP.gene.pca changing from SeuratCommand to SeuratCommand
#> [13:35:35] Clustering Cells...
#> Computing nearest neighbor graph
#> Computing SNN
#> Warning: Command FindNeighbors.gene.pca changing from SeuratCommand to SeuratCommand
#> clustering at 0.2 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.9536
#> Number of communities: 7
#> Elapsed time: 0 seconds
#> Warning: Command FindClusters changing from SeuratCommand to SeuratCommand
#> clustering at 0.4 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.9171
#> Number of communities: 9
#> Elapsed time: 0 seconds
#> 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: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8844
#> Number of communities: 9
#> Elapsed time: 0 seconds
#> clustering at 0.8 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8516
#> Number of communities: 9
#> Elapsed time: 0 seconds
#> clustering at 1 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8219
#> Number of communities: 11
#> Elapsed time: 0 seconds
#> clustering at 1.2 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7972
#> Number of communities: 12
#> Elapsed time: 0 seconds
#> clustering at 1.4 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7757
#> Number of communities: 13
#> Elapsed time: 0 seconds
#> clustering at 1.6 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7546
#> Number of communities: 13
#> Elapsed time: 0 seconds
#> clustering at 1.8 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7340
#> Number of communities: 13
#> Elapsed time: 0 seconds
#> clustering at 2 resolution
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#>
#> Number of nodes: 1011
#> Number of edges: 35554
#>
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.7134
#> Number of communities: 13
#> Elapsed time: 0 seconds