vignettes/introduction.md
In fpestana-git/clusteringR: Transcriptomic clustering functions
title: "introduction"
output:
html_document:
keep_md: true
self_contained: true
vignette: >
%\VignetteIndexEntry{introduction}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
Install packages
devtools::install_github("fpestana-git/clusteringR",force = F)
#> Skipping install of 'clusteringR' from a github remote, the SHA1 (5f4ed76a) has not changed since last install.
#> Use `force = TRUE` to force installation
devtools::install_github("fpestana-git/visualisR",force = F)
#> Skipping install of 'visualisR' from a github remote, the SHA1 (8b012896) has not changed since last install.
#> Use `force = TRUE` to force installation
library(clusteringR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(Seurat)
#> Attaching SeuratObject
library(visualisR)
library(ggplot2)
library(rliger)
#> Loading required package: cowplot
#> Loading required package: Matrix
#> Loading required package: patchwork
#>
#> Attaching package: 'patchwork'
#> The following object is masked from 'package:cowplot':
#>
#> align_plots
library(ggpubr)
#>
#> Attaching package: 'ggpubr'
#> The following object is masked from 'package:cowplot':
#>
#> get_legend
library(viridis)
#> Loading required package: viridisLite
Load data
# Load the PBMC dataset
pbmc <- Read10X(data.dir = "../data/pbmc/")
Clustering
# Run Seurat clustering using log normalization
seuratLOG <- clusteringSeurat(datasetObject = pbmc,datasetName = "test",metadataAvailable = F,mapTypeValue = "umap",normalizationMethod = "LOG")
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')
#> Centering and scaling data matrix
#> PC_ 1
#> Positive: MALAT1, LTB, IL32, CD2, ACAP1, STK17A, CTSW, CD247, CCL5, GIMAP5
#> AQP3, GZMA, CST7, TRAF3IP3, MAL, HOPX, ITM2A, GZMK, MYC, GIMAP7
#> BEX2, ETS1, LDLRAP1, ZAP70, LYAR, RIC3, TNFAIP8, NKG7, KLRG1, SAMD3
#> Negative: CST3, TYROBP, LST1, AIF1, FTL, FCN1, LYZ, FTH1, S100A9, FCER1G
#> TYMP, CFD, LGALS1, S100A8, CTSS, LGALS2, SERPINA1, SPI1, IFITM3, PSAP
#> CFP, SAT1, IFI30, COTL1, S100A11, NPC2, LGALS3, GSTP1, PYCARD, NCF2
#> PC_ 2
#> Positive: NKG7, PRF1, CST7, GZMA, GZMB, FGFBP2, CTSW, GNLY, GZMH, SPON2
#> CCL4, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX, CTSC
#> TTC38, S100A4, ANXA1, IL32, IGFBP7, ID2, ACTB, XCL1, APOBEC3G, SAMD3
#> Negative: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA, HLA-DQB1, LINC00926, CD79B, HLA-DRB1, CD74
#> HLA-DPB1, HLA-DMA, HLA-DQA2, HLA-DRB5, HLA-DPA1, HLA-DMB, FCRLA, HVCN1, LTB, BLNK
#> KIAA0125, P2RX5, IRF8, IGLL5, SWAP70, ARHGAP24, SMIM14, PPP1R14A, FCRL2, C16orf74
#> PC_ 3
#> Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, CD74, HLA-DPA1, MS4A1, HLA-DRB1, HLA-DRB5
#> HLA-DRA, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, HVCN1, FCRLA, IRF8, BLNK
#> KIAA0125, SMIM14, PLD4, P2RX5, IGLL5, SWAP70, LAT2, TMSB10, IGJ, MZB1
#> Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU
#> HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, CA2, PTCRA, ACRBP, MMD, NGFRAP1
#> TREML1, F13A1, RUFY1, SEPT5, MPP1, TSC22D1, CMTM5, RP11-367G6.3, MYL9, GP1BA
#> PC_ 4
#> Positive: VIM, S100A8, S100A6, S100A4, TMSB10, S100A9, IL32, GIMAP7, S100A10, LGALS2
#> RBP7, MAL, FCN1, LYZ, CD2, S100A12, MS4A6A, FYB, S100A11, AQP3
#> GIMAP4, FOLR3, ANXA1, MALAT1, AIF1, GIMAP5, IL8, IFI6, TRABD2A, ASGR1
#> Negative: HLA-DQA1, HIST1H2AC, PF4, CD79A, SDPR, CD79B, PPBP, GNG11, HLA-DQB1, SPARC
#> MS4A1, CD74, GP9, HLA-DPB1, RGS18, NRGN, PTCRA, CD9, HLA-DQA2, AP001189.4
#> CLU, TUBB1, CA2, HLA-DRB1, HLA-DPA1, ITGA2B, HLA-DRA, TCL1A, TMEM40, ACRBP
#> PC_ 5
#> Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1, CCL4, CST7, SPON2, GZMA, GZMH
#> CLIC3, XCL2, CTSW, TTC38, AKR1C3, CCL5, IGFBP7, XCL1, S100A8, CCL3
#> TYROBP, HOPX, CD160, HAVCR2, S100A9, FCER1G, PTGDR, LGALS2, RBP7, S100A12
#> Negative: LTB, VIM, AQP3, PPA1, MAL, KIAA0101, CD2, CORO1B, CYTIP, FYB
#> IL32, TRADD, ANXA5, TUBA1B, HN1, PTGES3, TYMS, ITM2A, COTL1, GPR183
#> ACTG1, TNFAIP8, ATP5C1, TRAF3IP3, GIMAP4, PRDX1, ZWINT, ABRACL, NGFRAP1, LDLRAP1
#> [1] "The best pca to use should be: 10"
#> 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
# Run Seurat clustering using SCT normalization
seuratSCT <- clusteringSeurat(datasetObject = pbmc,datasetName = "test",metadataAvailable = F,mapTypeValue = "umap",normalizationMethod = "SCT")
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')
#> Calculating cell attributes from input UMI matrix: log_umi
#> Variance stabilizing transformation of count matrix of size 12572 by 2700
#> Model formula is y ~ log_umi
#> Get Negative Binomial regression parameters per gene
#> Using 2000 genes, 1000 cells
#>
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#> Found 99 outliers - those will be ignored in fitting/regularization step
#> Second step: Get residuals using fitted parameters for 12572 genes
#>
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#> Computing corrected count matrix for 12572 genes
#>
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#> Calculating gene attributes
#> Wall clock passed: Time difference of 14.72622 secs
#> Determine variable features
#> Place corrected count matrix in counts slot
#> Centering and scaling data matrix
#> Set default assay to SCT
#> PC_ 1
#> Positive: FTL, CST3, FTH1, TYROBP, LYZ, AIF1, LST1, LGALS1, FCER1G, CTSS
#> FCN1, S100A9, TYMP, COTL1, S100A6, PSAP, SAT1, S100A4, S100A11, CFD
#> CD68, OAZ1, S100A8, NPC2, SERPINA1, SPI1, IFITM3, GABARAP, LGALS2, GSTP1
#> Negative: RPS27, RPS27A, MALAT1, RPL21, RPL3, RPS3A, RPL9, RPL13A, RPS12, RPS15A
#> RPSA, RPS6, RPS3, RPS18, RPL13, LDHB, CD3D, RPS13, LTB, RPS14
#> RPL5, CD3E, RPL32, RPS4X, PTPRCAP, RPS8, IL32, RPL10A, RPL7, RPL10
#> PC_ 2
#> Positive: RPL32, RPL18A, RPL10, RPL13, RPL11, RPS2, HLA-DRA, RPL13A, RPS27, RPS18
#> CD74, RPLP1, RPS12, CD79A, RPS14, RPS5, HLA-DQA1, RPS16, RPS9, HLA-DQB1
#> HLA-DPB1, RPS6, RPS13, HLA-DRB1, RPL19, RPL10A, RPL18, CD37, MS4A1, RPS8
#> Negative: NKG7, GZMB, PRF1, CST7, GZMA, GNLY, FGFBP2, CTSW, B2M, SPON2
#> HLA-C, CCL5, CCL4, GZMH, CD247, GZMM, KLRD1, CLIC3, HLA-A, XCL2
#> AKR1C3, S1PR5, TTC38, HOPX, APMAP, PRSS23, IGFBP7, TPST2, GPR56, FCGR3A
#> PC_ 3
#> Positive: NKG7, PRF1, GZMA, CST7, GNLY, CTSW, FGFBP2, S100A4, GZMB, CCL4
#> SPON2, S100A6, TYROBP, GZMM, CD247, ID2, GZMH, HCST, IGFBP7, KLRD1
#> ITGB2, LGALS1, PFN1, ANXA1, S100A11, CYBA, S100A9, FCN1, LYZ, S100A10
#> Negative: GP9, AP001189.4, LY6G6F, ITGA2B, TMEM40, PF4, GNG11, SDPR, PTCRA, TREML1
#> SPARC, CLDN5, PPBP, SEPT5, CLU, HGD, ITGB3, CMTM5, C2orf88, RP11-879F14.2
#> HIST1H2AC, TUBB1, SCGB1C1, NRGN, GP1BA, ACRBP, RGS18, CLEC1B, CD9, CA2
#> PC_ 4
#> Positive: LDHB, CD3D, CD3E, IL7R, RPS14, NOSIP, JUNB, RPS12, RGS10, TMSB4X
#> VIM, FYB, IL32, NGFRAP1, ZFP36L2, GIMAP7, RGCC, RPS3, GIMAP4, MAL
#> RPL32, CD2, TPT1, AQP3, S100A4, TCF7, FOS, TMEM66, FLT3LG, S100A6
#> Negative: CD74, CD79A, CD79B, HLA-DRA, HLA-DQA1, MS4A1, HLA-DQB1, HLA-DPB1, TCL1A, HLA-DRB1
#> HLA-DPA1, LINC00926, CD37, VPREB3, HLA-DQA2, HLA-DRB5, BANK1, FCER2, HLA-DMA, FCRLA
#> HVCN1, HLA-DMB, TSPAN13, HLA-DOB, EAF2, CD72, SPIB, PKIG, BLNK, GNG7
#> PC_ 5
#> Positive: S100A8, S100A9, LYZ, GPX1, LGALS2, CD14, GSTP1, MS4A6A, FOLR3, BLVRB
#> CSF3R, GRN, CEBPD, S100A12, GAPDH, VCAN, QPCT, ID1, ALDH2, RBP7
#> ASGR1, RNASE6, AP1S2, CCL3, PLBD1, BST1, CLEC4E, MGST1, TSPO, NCF1
#> Negative: FCGR3A, MS4A7, IFITM2, RHOC, RP11-290F20.3, CKB, CDKN1C, RPS19, SIGLEC10, LST1
#> HMOX1, LRRC25, IFITM3, LILRA3, VMO1, FCER1G, PILRA, TIMP1, CTD-2006K23.1, HCK
#> LYPD2, COTL1, CEBPB, FAM110A, SERPINA1, AIF1, PPM1N, WARS, ADA, LILRB2
#> [1] "The best pca to use should be: 10"
# Run LIGER clustering using iNMF algorythm
ligerClustering <- clusteringLiger(datasets = list(pbmc = pbmc),referenceDatasetName = "pbmc", useiNMF = T)
#> Removing 16104 genes not expressing in pbmc.
#>
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#> Finished in 2.529688 mins, 50 iterations.
#> Max iterations set: 50.
#> Final objective delta: 1.624526e-05.
#> Best results with seed 1.
#> Louvain Clustering on quantile normalized cell factor loadings.
Visualize Dim plot
drawDimPlot(seuratObject = seuratLOG,datasetName = "pbmc")
drawDimPlot(seuratObject = seuratSCT,datasetName = "pbmc")
Visualize Feature plot
drawFeaturePlot(seuratObject = seuratLOG,
featureValues = c("S100A9","NKG7","LDHB","CD79A"),
nrowValue = 2,
ncolValue = 2,
datasetName = "test")
#> [1] "1 of 4 total features done. S100A9"
#> [1] "2 of 4 total features done. NKG7"
#> [1] "3 of 4 total features done. LDHB"
#> [1] "4 of 4 total features done. CD79A"
Visualize Heatmap
# Visualize the variable features
drawHeatmapPlot(seuratObject = seuratLOG,featureNames = c("S100A9","NKG7","LDHB","CD79A"),assaytype = "RNA",plotName = "test",groupValue = "seurat_clusters",drawLinesValue = T)
#> Scale for 'fill' is already present. Adding another scale for 'fill', which
#> will replace the existing scale.
Visualize Dot plot
drawDotPlot(seuratObject = seuratLOG,plotName = "test",featureValues = c("S100A9","NKG7","LDHB","CD79A"))
fpestana-git/clusteringR documentation built on May 3, 2022, 11:59 a.m.
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title: "introduction" output: html_document: keep_md: true self_contained: true vignette: > %\VignetteIndexEntry{introduction} %\VignetteEngine{knitr::rmarkdown} %\VignetteEncoding{UTF-8}
Install packages
devtools::install_github("fpestana-git/clusteringR",force = F)
#> Skipping install of 'clusteringR' from a github remote, the SHA1 (5f4ed76a) has not changed since last install.
#> Use `force = TRUE` to force installation
devtools::install_github("fpestana-git/visualisR",force = F)
#> Skipping install of 'visualisR' from a github remote, the SHA1 (8b012896) has not changed since last install.
#> Use `force = TRUE` to force installation
library(clusteringR)
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
library(Seurat)
#> Attaching SeuratObject
library(visualisR)
library(ggplot2)
library(rliger)
#> Loading required package: cowplot
#> Loading required package: Matrix
#> Loading required package: patchwork
#>
#> Attaching package: 'patchwork'
#> The following object is masked from 'package:cowplot':
#>
#> align_plots
library(ggpubr)
#>
#> Attaching package: 'ggpubr'
#> The following object is masked from 'package:cowplot':
#>
#> get_legend
library(viridis)
#> Loading required package: viridisLite
Load data
# Load the PBMC dataset
pbmc <- Read10X(data.dir = "../data/pbmc/")
Clustering
# Run Seurat clustering using log normalization
seuratLOG <- clusteringSeurat(datasetObject = pbmc,datasetName = "test",metadataAvailable = F,mapTypeValue = "umap",normalizationMethod = "LOG")
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')
#> Centering and scaling data matrix
#> PC_ 1
#> Positive: MALAT1, LTB, IL32, CD2, ACAP1, STK17A, CTSW, CD247, CCL5, GIMAP5
#> AQP3, GZMA, CST7, TRAF3IP3, MAL, HOPX, ITM2A, GZMK, MYC, GIMAP7
#> BEX2, ETS1, LDLRAP1, ZAP70, LYAR, RIC3, TNFAIP8, NKG7, KLRG1, SAMD3
#> Negative: CST3, TYROBP, LST1, AIF1, FTL, FCN1, LYZ, FTH1, S100A9, FCER1G
#> TYMP, CFD, LGALS1, S100A8, CTSS, LGALS2, SERPINA1, SPI1, IFITM3, PSAP
#> CFP, SAT1, IFI30, COTL1, S100A11, NPC2, LGALS3, GSTP1, PYCARD, NCF2
#> PC_ 2
#> Positive: NKG7, PRF1, CST7, GZMA, GZMB, FGFBP2, CTSW, GNLY, GZMH, SPON2
#> CCL4, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX, CTSC
#> TTC38, S100A4, ANXA1, IL32, IGFBP7, ID2, ACTB, XCL1, APOBEC3G, SAMD3
#> Negative: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA, HLA-DQB1, LINC00926, CD79B, HLA-DRB1, CD74
#> HLA-DPB1, HLA-DMA, HLA-DQA2, HLA-DRB5, HLA-DPA1, HLA-DMB, FCRLA, HVCN1, LTB, BLNK
#> KIAA0125, P2RX5, IRF8, IGLL5, SWAP70, ARHGAP24, SMIM14, PPP1R14A, FCRL2, C16orf74
#> PC_ 3
#> Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, CD74, HLA-DPA1, MS4A1, HLA-DRB1, HLA-DRB5
#> HLA-DRA, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, HVCN1, FCRLA, IRF8, BLNK
#> KIAA0125, SMIM14, PLD4, P2RX5, IGLL5, SWAP70, LAT2, TMSB10, IGJ, MZB1
#> Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU
#> HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, CA2, PTCRA, ACRBP, MMD, NGFRAP1
#> TREML1, F13A1, RUFY1, SEPT5, MPP1, TSC22D1, CMTM5, RP11-367G6.3, MYL9, GP1BA
#> PC_ 4
#> Positive: VIM, S100A8, S100A6, S100A4, TMSB10, S100A9, IL32, GIMAP7, S100A10, LGALS2
#> RBP7, MAL, FCN1, LYZ, CD2, S100A12, MS4A6A, FYB, S100A11, AQP3
#> GIMAP4, FOLR3, ANXA1, MALAT1, AIF1, GIMAP5, IL8, IFI6, TRABD2A, ASGR1
#> Negative: HLA-DQA1, HIST1H2AC, PF4, CD79A, SDPR, CD79B, PPBP, GNG11, HLA-DQB1, SPARC
#> MS4A1, CD74, GP9, HLA-DPB1, RGS18, NRGN, PTCRA, CD9, HLA-DQA2, AP001189.4
#> CLU, TUBB1, CA2, HLA-DRB1, HLA-DPA1, ITGA2B, HLA-DRA, TCL1A, TMEM40, ACRBP
#> PC_ 5
#> Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1, CCL4, CST7, SPON2, GZMA, GZMH
#> CLIC3, XCL2, CTSW, TTC38, AKR1C3, CCL5, IGFBP7, XCL1, S100A8, CCL3
#> TYROBP, HOPX, CD160, HAVCR2, S100A9, FCER1G, PTGDR, LGALS2, RBP7, S100A12
#> Negative: LTB, VIM, AQP3, PPA1, MAL, KIAA0101, CD2, CORO1B, CYTIP, FYB
#> IL32, TRADD, ANXA5, TUBA1B, HN1, PTGES3, TYMS, ITM2A, COTL1, GPR183
#> ACTG1, TNFAIP8, ATP5C1, TRAF3IP3, GIMAP4, PRDX1, ZWINT, ABRACL, NGFRAP1, LDLRAP1
#> [1] "The best pca to use should be: 10"
#> 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
# Run Seurat clustering using SCT normalization
seuratSCT <- clusteringSeurat(datasetObject = pbmc,datasetName = "test",metadataAvailable = F,mapTypeValue = "umap",normalizationMethod = "SCT")
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')
#> Calculating cell attributes from input UMI matrix: log_umi
#> Variance stabilizing transformation of count matrix of size 12572 by 2700
#> Model formula is y ~ log_umi
#> Get Negative Binomial regression parameters per gene
#> Using 2000 genes, 1000 cells
#>
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#> Found 99 outliers - those will be ignored in fitting/regularization step
#> Second step: Get residuals using fitted parameters for 12572 genes
#>
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#> Computing corrected count matrix for 12572 genes
#>
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#> Calculating gene attributes
#> Wall clock passed: Time difference of 14.72622 secs
#> Determine variable features
#> Place corrected count matrix in counts slot
#> Centering and scaling data matrix
#> Set default assay to SCT
#> PC_ 1
#> Positive: FTL, CST3, FTH1, TYROBP, LYZ, AIF1, LST1, LGALS1, FCER1G, CTSS
#> FCN1, S100A9, TYMP, COTL1, S100A6, PSAP, SAT1, S100A4, S100A11, CFD
#> CD68, OAZ1, S100A8, NPC2, SERPINA1, SPI1, IFITM3, GABARAP, LGALS2, GSTP1
#> Negative: RPS27, RPS27A, MALAT1, RPL21, RPL3, RPS3A, RPL9, RPL13A, RPS12, RPS15A
#> RPSA, RPS6, RPS3, RPS18, RPL13, LDHB, CD3D, RPS13, LTB, RPS14
#> RPL5, CD3E, RPL32, RPS4X, PTPRCAP, RPS8, IL32, RPL10A, RPL7, RPL10
#> PC_ 2
#> Positive: RPL32, RPL18A, RPL10, RPL13, RPL11, RPS2, HLA-DRA, RPL13A, RPS27, RPS18
#> CD74, RPLP1, RPS12, CD79A, RPS14, RPS5, HLA-DQA1, RPS16, RPS9, HLA-DQB1
#> HLA-DPB1, RPS6, RPS13, HLA-DRB1, RPL19, RPL10A, RPL18, CD37, MS4A1, RPS8
#> Negative: NKG7, GZMB, PRF1, CST7, GZMA, GNLY, FGFBP2, CTSW, B2M, SPON2
#> HLA-C, CCL5, CCL4, GZMH, CD247, GZMM, KLRD1, CLIC3, HLA-A, XCL2
#> AKR1C3, S1PR5, TTC38, HOPX, APMAP, PRSS23, IGFBP7, TPST2, GPR56, FCGR3A
#> PC_ 3
#> Positive: NKG7, PRF1, GZMA, CST7, GNLY, CTSW, FGFBP2, S100A4, GZMB, CCL4
#> SPON2, S100A6, TYROBP, GZMM, CD247, ID2, GZMH, HCST, IGFBP7, KLRD1
#> ITGB2, LGALS1, PFN1, ANXA1, S100A11, CYBA, S100A9, FCN1, LYZ, S100A10
#> Negative: GP9, AP001189.4, LY6G6F, ITGA2B, TMEM40, PF4, GNG11, SDPR, PTCRA, TREML1
#> SPARC, CLDN5, PPBP, SEPT5, CLU, HGD, ITGB3, CMTM5, C2orf88, RP11-879F14.2
#> HIST1H2AC, TUBB1, SCGB1C1, NRGN, GP1BA, ACRBP, RGS18, CLEC1B, CD9, CA2
#> PC_ 4
#> Positive: LDHB, CD3D, CD3E, IL7R, RPS14, NOSIP, JUNB, RPS12, RGS10, TMSB4X
#> VIM, FYB, IL32, NGFRAP1, ZFP36L2, GIMAP7, RGCC, RPS3, GIMAP4, MAL
#> RPL32, CD2, TPT1, AQP3, S100A4, TCF7, FOS, TMEM66, FLT3LG, S100A6
#> Negative: CD74, CD79A, CD79B, HLA-DRA, HLA-DQA1, MS4A1, HLA-DQB1, HLA-DPB1, TCL1A, HLA-DRB1
#> HLA-DPA1, LINC00926, CD37, VPREB3, HLA-DQA2, HLA-DRB5, BANK1, FCER2, HLA-DMA, FCRLA
#> HVCN1, HLA-DMB, TSPAN13, HLA-DOB, EAF2, CD72, SPIB, PKIG, BLNK, GNG7
#> PC_ 5
#> Positive: S100A8, S100A9, LYZ, GPX1, LGALS2, CD14, GSTP1, MS4A6A, FOLR3, BLVRB
#> CSF3R, GRN, CEBPD, S100A12, GAPDH, VCAN, QPCT, ID1, ALDH2, RBP7
#> ASGR1, RNASE6, AP1S2, CCL3, PLBD1, BST1, CLEC4E, MGST1, TSPO, NCF1
#> Negative: FCGR3A, MS4A7, IFITM2, RHOC, RP11-290F20.3, CKB, CDKN1C, RPS19, SIGLEC10, LST1
#> HMOX1, LRRC25, IFITM3, LILRA3, VMO1, FCER1G, PILRA, TIMP1, CTD-2006K23.1, HCK
#> LYPD2, COTL1, CEBPB, FAM110A, SERPINA1, AIF1, PPM1N, WARS, ADA, LILRB2
#> [1] "The best pca to use should be: 10"
# Run LIGER clustering using iNMF algorythm
ligerClustering <- clusteringLiger(datasets = list(pbmc = pbmc),referenceDatasetName = "pbmc", useiNMF = T)
#> Removing 16104 genes not expressing in pbmc.
#>
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#> Finished in 2.529688 mins, 50 iterations.
#> Max iterations set: 50.
#> Final objective delta: 1.624526e-05.
#> Best results with seed 1.
#> Louvain Clustering on quantile normalized cell factor loadings.
Visualize Dim plot
drawDimPlot(seuratObject = seuratLOG,datasetName = "pbmc")
drawDimPlot(seuratObject = seuratSCT,datasetName = "pbmc")
Visualize Feature plot
drawFeaturePlot(seuratObject = seuratLOG,
featureValues = c("S100A9","NKG7","LDHB","CD79A"),
nrowValue = 2,
ncolValue = 2,
datasetName = "test")
#> [1] "1 of 4 total features done. S100A9"
#> [1] "2 of 4 total features done. NKG7"
#> [1] "3 of 4 total features done. LDHB"
#> [1] "4 of 4 total features done. CD79A"
Visualize Heatmap
# Visualize the variable features
drawHeatmapPlot(seuratObject = seuratLOG,featureNames = c("S100A9","NKG7","LDHB","CD79A"),assaytype = "RNA",plotName = "test",groupValue = "seurat_clusters",drawLinesValue = T)
#> Scale for 'fill' is already present. Adding another scale for 'fill', which
#> will replace the existing scale.
Visualize Dot plot
drawDotPlot(seuratObject = seuratLOG,plotName = "test",featureValues = c("S100A9","NKG7","LDHB","CD79A"))
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