A wrapper function run complete Seurat workflow.

runSeurat(inSCE, useAssay)

Arguments

inSCE	Input `SingleCellExperiment` object.
useAssay	Specify the assay to use with Seurat workflow.

Value

SingleCellExperiment with results from Seurat workflow stored.

Examples

data(sce_chcl, package = "scds")
sce_chcl <- runSeurat(inSCE = sce_chcl, useAssay = "counts")
#> Normalizing Data
#> Scaling Data
#> Centering and scaling data matrix
#> Identifying highly variable genes
#> Computing reduced dimensions
#> PC_ 1 
#> Positive:  SLC25A4, NT5C3B, CYSTM1, RHOBTB3, TPD52L1, MRPL41, NDUFA8, LSM4, SYAP1, BCL2L12 
#> 	   UCHL3, COPS6, ABCF1, CCT4, ATP5B, NDUFB7, KXD1, MRPL37, POLR2I, ATP6V0E2 
#> 	   ZC3H15, LSM10, COA6, FAM162A, FAM50A, SERBP1, MAZ, COX7B, WBSCR22, SNRPC 
#> Negative:  LST1, CTSG, AZU1, PLEK, S100A6, IGFBP7, PRSS57, MS4A3, PLD4, NKG7 
#> 	   INPP5D, MYO1G, CD34, GLIPR1, CD84, DOCK2, FTL, GPSM3, NUPR1, ABRACL 
#> 	   ZFP36L2, TMEM30A, RGS18, CD4, TESPA1, FAM43A, PBX3, RSAD2, SAMD9L, UTS2 
#> PC_ 2 
#> Positive:  PRAME, HIST1H2BJ, CTCFL, SMIM1, AHSP, HK1, SLC25A37, FTL, LINC00221, HIC2 
#> 	   LINC00152, SLC43A3, NOSTRIN, AC069277.2, TNNT1, ATF7IP2, PIM1, FADS1, USP20, HSPB1 
#> 	   CCND3, NNMT, SLC39A4, MYLIP, LGALS8, CYSTM1, BCAP29, G6PD, ST8SIA6-AS1, CXXC5 
#> Negative:  LYPD1, ATP6V0E2, CSRP2, RP11-834C11.4, NKX2-5, WDR54, H1F0, MESP1, ABRACL, PDK2 
#> 	   CMTM8, DBI, OSBPL1A, CDH2, ZSWIM7, TENM3, LRPAP1, AMOT, ADI1, FBLN1 
#> 	   MZT1, FZD3, MRPL43, NT5C3B, LGALS3BP, HOXA11, KCTD12, GTSE1, CBX1, SPOP 
#> PC_ 3 
#> Positive:  SLC25A4, NT5C3B, CRNDE, CYSTM1, RHOBTB3, CSRP2, TPD52L1, SRP19, ATP6V0E2, LYPD1 
#> 	   MAP1B, SYAP1, ANXA5, HTATIP2, COPS6, PRAME, APOC1, RP11-834C11.4, C17orf76-AS1, HSPB1 
#> 	   MRPL41, TNNT1, AMOT, NKX2-5, SEPP1, C1orf56, H1F0, FAM89A, DDAH2, ZNF580 
#> Negative:  PRSS57, CD34, IGFBP7, NKG7, GGH, PSME1, KLRG1, IQGAP2, PLEK, SUSD3 
#> 	   ABRACL, MATK, MYO1G, FAM49B, RSAD2, LST1, EMC2, MALAT1, PLEKHA2, BACE2 
#> 	   ZFP36L2, DOCK2, TESPA1, FTL, SP110, TRBC2, DBI, MT-RNR2, PLD4, SLC39A4 
#> PC_ 4 
#> Positive:  PRSS57, CD34, BACE2, IQGAP2, KLRG1, PIM1, GSTO1, DNAJC9, ANXA7, RSAD2 
#> 	   MYO1G, ATF7IP2, CBX2, CD84, SIGIRR, TESPA1, MRPL43, CASP6, MATK, NKG7 
#> 	   ZNF626, ABCF1, MRPL16, SSBP2, SLC25A4, PLEKHA2, MT-ND6, HK1, CYSTM1, FST 
#> Negative:  AZU1, PPT1, MS4A3, CTSG, S100A6, SMAP2, TMEM30A, CRNDE, LST1, HSPB1 
#> 	   RPS6, BEX1, CITED2, SPCS1, ABRACL, PPDPF, NID1, CD4, TIMP3, OAZ1 
#> 	   SCCPDH, MRPL18, APOC1, FTL, RPS16, PCCA, GPSM3, PLD4, MRPL37, SNHG5 
#> PC_ 5 
#> Positive:  GNB2L1, C17orf76-AS1, RPL13, ATP5B, EIF3K, SNRPC, RPL13A, NDUFA8, RPS16, OAZ1 
#> 	   RPS6, MRPL16, COX7B, UQCR10, NDUFB6, CCT4, APEX1, AAMP, LSM4, RPL32 
#> 	   GSTO1, SRI, MAD2L1, NDUFS5, SERBP1, TRMT112, COPS6, SLBP, MRPL37, NRP2 
#> Negative:  MALAT1, MT-ND2, MT-RNR2, MT-ND3, MT-ND6, DST, RREB1, GOLGB1, DBI, RBM12B 
#> 	   N4BP2L2, BAZ1B, KIF14, ARHGEF9, ITCH, PLEK, CEP350, CELF1, NKTR, HELZ 
#> 	   AMOT, TMTC2, UBN2, MAP4K4, FAM89A, DLGAP4, ZNF638, ANKRD6, MYLIP, APOC1 
#> Computing tSNE/UMAP
#> 11:14:10 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’
#> 11:14:10 Read 2000 rows and found 10 numeric columns
#> 11:14:10 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’
#> 11:14:10 Building Annoy index with metric = cosine, n_trees = 50
#> 0%   10   20   30   40   50   60   70   80   90   100%
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#> 11:14:10 Writing NN index file to temp file /var/folders/8g/zr_0d8wd23762jsqlwm5r_6w0000gn/T//RtmpLQbUic/file17ea537a13d3d
#> 11:14:10 Searching Annoy index using 1 thread, search_k = 3000
#> 11:14:10 Annoy recall = 100%
#> 11:14:11 Commencing smooth kNN distance calibration using 1 thread
#> 11:14:13 Initializing from normalized Laplacian + noise
#> 11:14:13 Commencing optimization for 500 epochs, with 81222 positive edges
#> 11:14:16 Optimization finished
#> Identifying clusters in data
#> Computing nearest neighbor graph
#> Computing SNN
#> Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck
#> 
#> Number of nodes: 2000
#> Number of edges: 76126
#> 
#> Running Louvain algorithm...
#> Maximum modularity in 10 random starts: 0.8269
#> Number of communities: 8
#> Elapsed time: 0 seconds
#> Identifying marker genes in data
#> Calculating cluster 0
#> Calculating cluster 1
#> Calculating cluster 2
#> Calculating cluster 3
#> Calculating cluster 4
#> Calculating cluster 5
#> Calculating cluster 6
#> Calculating cluster 7