R Notebook

tissue_of_interest = "Liver"
library(here)
source("/restricted/projectnb/waxmanlab/kkarri/scRNAseq_data_integration/boilerplate.R")
#tiss = load_tissue_droplet(tissue_of_interest)
#library(scater)
library(dplyr)
library(Seurat)
library(cowplot)
#library(MAST)

aggregate G185M3 and G185 M4 which are dcas9 and lnc5998 samples.

## aggregate G185M3 and G185 M4 which are dcas9 and lnc5998 samples. 

samples1 <- read.csv(file.path("/restricted/projectnb/waxmanlab/kkarri2/cellranger_KO_aggr2/outs/aggregation.csv"), stringsAsFactors=F)
raw.data1 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/cellranger_KO_aggr2/outs/filtered_feature_bc_matrix")
G185M3M4 <- CreateSeuratObject(raw.data1)   # dropseq

cellcodes <- as.data.frame(G185M3M4@assays$RNA@counts@Dimnames[[2]])
colnames(cellcodes) <- "barcodes"
rownames(cellcodes) <- cellcodes$barcodes
cellcodes$libcodes <- as.factor(gsub(pattern=".+-", replacement="", cellcodes$barcodes))
cellcodes$samples <- as.vector(samples1$library_id[cellcodes$libcodes])
sampleidentity <- cellcodes["samples"]

G185M3M4 <- AddMetaData(object=G185M3M4,sampleidentity)
ctrl_aggr1 <- G185M3M4 
ctrl_aggr1 <-  subset(ctrl_aggr1, subset = nFeature_RNA > 200 & nCount_RNA > 500)
ctrl_aggr1 <- NormalizeData(ctrl_aggr1, verbose = FALSE)
ctrl_aggr1 <- FindVariableFeatures(ctrl_aggr1, selection.method = "vst", nfeatures = 2000)
ctrl_aggr1 <- SCTransform(ctrl_aggr1)
ctrl_aggr1[["percent.mt"]] <- PercentageFeatureSet(ctrl_aggr1, pattern = "^mt-")
DefaultAssay(ctrl_aggr1) <- "SCT"
ctrl_aggr1 <- ScaleData(ctrl_aggr1,vars.to.regress = "percent.mt", verbose = FALSE)
ctrl_aggr1 <- RunPCA(ctrl_aggr1, npcs = 100, verbose = FALSE)
ctrl_aggr1 <- RunUMAP(ctrl_aggr1, reduction = "pca", dims = 1:30)
ctrl_aggr1 <- FindNeighbors(ctrl_aggr1, reduction = "pca", dims = 1:30)
ctrl_aggr1 <- FindClusters(ctrl_aggr1, resolution = 0.2)   

ctrl_aggr1_p1.1 <- DimPlot(ctrl_aggr1, reduction = "umap", label = TRUE)  
ctrl_aggr1_p1.2 <- DimPlot(ctrl_aggr1, reduction = "umap", split.by = "samples",label = TRUE)   
ctrl_aggr1_p1.3 <- DimPlot(ctrl_aggr1, reduction = "umap", group.by = "samples") 
plot_grid(ctrl_aggr1_p1.1,ctrl_aggr1_p1.2)

MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1', 'Glul', 'Oat', 'Gulo',
              'Ass1', 'Hamp', 'Gstp1', 'Ubb',
              'Cyp2f2', 'Pck1', 'Hal', 'Cdh1', 'Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')



DefaultAssay(ctrl_aggr1) <- "RNA"
ctrl_aggr1 <- NormalizeData(ctrl_aggr1)
ctrl_aggr1_d1.1 <-DotPlot(ctrl_aggr1, features = MS_list2)+RotatedAxis()
ctrl_aggr1_d1.2 <-DotPlot(ctrl_aggr1, features = "Newlnc-inter-c7-5998",split.by = "samples")   
ctrl_aggr1_v1.1<- VlnPlot(ctrl_aggr1, features = "Newlnc-inter-c7-5998", split.by = "samples", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)

plot_grid(ctrl_aggr1_p1.1,ctrl_aggr1_d1.1)
plot_grid(ctrl_aggr1_p1.1,ctrl_aggr1_p1.2,ctrl_aggr1_d1.1)

######## Get some numbers and stats for avergage expression ######
table(FetchData(ctrl_aggr1, c('samples','ident')))
x <- (AverageExpression(KO.cells, verbose = TRUE, assays = "RNA" ,slot="counts")$RNA)
x["ncRNA-inter-chr7-5998",]

save(ctrl_aggr1,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M3M4_subhep_SCT_PC30_res0.2_GB.Rdata")

ctrl_aggr29_subhep <- subset(ctrl_aggr1,idents = c('0'))

ctrl_aggr29_subhep <- NormalizeData(ctrl_aggr29_subhep)
ctrl_aggr29_subhep <- FindVariableFeatures(ctrl_aggr29_subhep, selection.method = "vst", nfeatures = 2000)
ctrl_aggr29_subhep$stim <- "ctrl_aggr29_subhep"
ctrl_aggr29_subhep <- SCTransform(ctrl_aggr29_subhep)  # check to see if it remove batch effects
ctrl_aggr29_subhep[["percent.mt"]] <- PercentageFeatureSet(ctrl_aggr29_subhep, pattern = "^mt-")
DefaultAssay(ctrl_aggr29_subhep) <- "SCT"

ctrl_aggr29_subhep <- ScaleData(ctrl_aggr29_subhep, vars.to.regress = "percent.mt")
ctrl_aggr29_subhep <- RunPCA(ctrl_aggr29_subhep, npcs = 100)
ctrl_aggr29_subhep <- RunUMAP(ctrl_aggr29_subhep, reduction = "pca", dims = 1:10)
ctrl_aggr29_subhep <- FindNeighbors(ctrl_aggr29_subhep, reduction = "pca", dims = 1:10)
ctrl_aggr29_subhep <- FindClusters(ctrl_aggr29_subhep, resolution = 0.2)

ctrl_aggr29_subhep_p1.1 <- DimPlot(ctrl_aggr29_subhep, label=TRUE)
ctrl_aggr29_subhep_p1.2 <- DimPlot(ctrl_aggr29_subhep,group.by = "samples", label=TRUE)
plot_grid(ctrl_aggr29_subhep_p1.1,ctrl_aggr29_subhep_p1.2)

DefaultAssay(ctrl_aggr29_subhep) <- "RNA"
ctrl_aggr29_subhep <- NormalizeData(ctrl_aggr29_subhep)
ctrl_aggr29_subhep_d1.1 <-  DotPlot(ctrl_aggr29_subhep,features = "Newlnc-inter-c7-5998") +RotatedAxis()
ctrl_aggr29_subhep_f1.1 <-  FeaturePlot(ctrl_aggr29_subhep,features = "Newlnc-inter-c7-5998") +RotatedAxis()
ctrl_aggr29_subhep_v1.1 <-  VlnPlot(ctrl_aggr29_subhep,features = "Newlnc-inter-c7-5998",sort = TRUE,split.by = "samples",pt.size = 0) +RotatedAxis()

table(FetchData(ctrl_aggr29_subhep, c('samples','ident')))
save(ctrl_aggr29_subhep,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M3M4_subhep_SCT_PC10_res0.2_GB.Rdata")

aggregate G185M3 and G185 M5 which are dcas9 and lnc5998 samples.

## aggregate G185M3 and G185 M5 which are dcas9 and lnc5998 samples. 
samples2 <- read.csv(file.path("/restricted/projectnb/waxmanlab/kkarri2/cellranger_KO_aggr3/outs/aggregation.csv"), stringsAsFactors=F)

#### aggreagte genebody counts folder from cellranger for G185M3 and G185M5 aggregated together
raw.data2 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/cellranger_KO_aggr3/outs/filtered_feature_bc_matrix")
G185M3M5 <- CreateSeuratObject(raw.data2)   # dropseq

cellcodes <- as.data.frame(G185M3M5@assays$RNA@counts@Dimnames[[2]])
colnames(cellcodes) <- "barcodes"
rownames(cellcodes) <- cellcodes$barcodes
cellcodes$libcodes <- as.factor(gsub(pattern=".+-", replacement="", cellcodes$barcodes))
cellcodes$samples <- as.vector(samples2$library_id[cellcodes$libcodes])
sampleidentity <- cellcodes["samples"]

G185M3M5 <- AddMetaData(object=G185M3M5,sampleidentity)
ctrl_aggr2 <- G185M3M5 

ctrl_aggr2 <-  subset(ctrl_aggr2, subset = nFeature_RNA > 200 & nCount_RNA > 500)
ctrl_aggr2 <- NormalizeData(ctrl_aggr2, verbose = FALSE)
ctrl_aggr2 <- FindVariableFeatures(ctrl_aggr2, selection.method = "vst", nfeatures = 2000)
ctrl_aggr2 <- SCTransform(ctrl_aggr2)
ctrl_aggr2[["percent.mt"]] <- PercentageFeatureSet(ctrl_aggr2, pattern = "^mt-")
DefaultAssay(ctrl_aggr2) <- "SCT"
ctrl_aggr2 <- ScaleData(ctrl_aggr2,vars.to.regress = "percent.mt", verbose = FALSE)
ctrl_aggr2 <- RunPCA(ctrl_aggr2, npcs = 100, verbose = FALSE)
ctrl_aggr2 <- RunUMAP(ctrl_aggr2, reduction = "pca", dims = 1:11)
ctrl_aggr2 <- FindNeighbors(ctrl_aggr2, reduction = "pca", dims = 1:11)
ctrl_aggr2 <- FindClusters(ctrl_aggr2, resolution = 0.5)   

ctrl_aggr2_p1.1 <- DimPlot(ctrl_aggr2, reduction = "umap", label = TRUE)  
ctrl_aggr2_p1.2 <- DimPlot(ctrl_aggr2, reduction = "umap", split.by = "samples",label = TRUE)   
ctrl_aggr2_p1.3 <- DimPlot(ctrl_aggr2, reduction = "umap", group.by = "samples") 
plot_grid(ctrl_aggr2_p1.1,ctrl_aggr2_p1.2)

MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')



DefaultAssay(ctrl_aggr2) <- "RNA"
ctrl_aggr2 <- ScaleData(ctrl_aggr2)

ctrl_aggr2 <- NormalizeData(ctrl_aggr2)

ctrl_aggr2_d1.1 <-DotPlot(ctrl_aggr2, features = MS_list2)+RotatedAxis()
ctrl_aggr2_d1.2 <-DotPlot(ctrl_aggr2, features = "(Gm13021)Newlnc-inter-c4-3779",split.by = "samples", cols=c("red","blue"))   
ctrl_aggr2_v1.1<- VlnPlot(ctrl_aggr2, features = "(Gm13021)Newlnc-inter-c4-3779", split.by = "samples", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)

ctrl_aggr2_h11 <- DoHeatmap(ctrl_aggr2, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), size = 3,assay = "RNA",slot = 'data')

ctrl_aggr2_f1.2 <- FeaturePlot(ctrl_aggr2,features = c('(Gm13021)Newlnc-inter-c4-3779','Newlnc-inter-c7-5998'),order=TRUE,label = TRUE)

plot_grid(ctrl_aggr2_p1.1,ctrl_aggr2_d1.1)
plot_grid(ctrl_aggr2_p1.1,ctrl_aggr2_p1.2,ctrl_aggr2_d1.1)

######## Get some numbers and stats for avergage expression ######
table(FetchData(ctrl_aggr2, c('samples','ident')))
x <- (AverageExpression(KO.cells, verbose = TRUE, assays = "RNA" ,slot="counts")$RNA)
x["ncRNA-inter-chr7-5998",]

save(ctrl_aggr2,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M3M5_SCT_PC11_res0.5_GB.Rdata")

## aggregate G185M3 and G185 M5 which are dcas9 and lnc5998 samples. 
samples3 <- read.csv(file.path("/restricted/projectnb/waxmanlab/kkarri2/cellranger_KO_aggr4_GB/outs/aggregation.csv"), stringsAsFactors=F)

#### aggreagte genebody counts folder from cellranger for G185M3 and G185M5 aggregated together
raw.data3 <- Read10X("/restricted/projectnb/waxmanlab/kkarri2/cellranger_KO_aggr4_GB/outs/filtered_feature_bc_matrix")
G185M4M5 <- CreateSeuratObject(raw.data3)   # dropseq

cellcodes <- as.data.frame(G185M4M5@assays$RNA@counts@Dimnames[[2]])
colnames(cellcodes) <- "barcodes"
rownames(cellcodes) <- cellcodes$barcodes
cellcodes$libcodes <- as.factor(gsub(pattern=".+-", replacement="", cellcodes$barcodes))
cellcodes$samples <- as.vector(samples3$library_id[cellcodes$libcodes])
sampleidentity <- cellcodes["samples"]

G185M4M5 <- AddMetaData(object=G185M4M5,sampleidentity)
ctrl_aggr3 <- G185M4M5 

ctrl_aggr3 <-  subset(ctrl_aggr3, subset = nFeature_RNA > 200 & nCount_RNA > 500)
ctrl_aggr3 <- NormalizeData(ctrl_aggr3, verbose = FALSE)
ctrl_aggr3 <- FindVariableFeatures(ctrl_aggr3, selection.method = "vst", nfeatures = 2000)
ctrl_aggr3 <- SCTransform(ctrl_aggr3)
ctrl_aggr3[["percent.mt"]] <- PercentageFeatureSet(ctrl_aggr3, pattern = "^mt-")
DefaultAssay(ctrl_aggr3) <- "SCT"
ctrl_aggr3 <- ScaleData(ctrl_aggr3,vars.to.regress = "percent.mt", verbose = FALSE)
ctrl_aggr3 <- RunPCA(ctrl_aggr3, npcs = 100, verbose = FALSE)
ctrl_aggr3 <- RunUMAP(ctrl_aggr3, reduction = "pca", dims = 1:11)
ctrl_aggr3 <- FindNeighbors(ctrl_aggr3, reduction = "pca", dims = 1:11)
ctrl_aggr3 <- FindClusters(ctrl_aggr3, resolution = 0.3)   

ctrl_aggr3_p1.1 <- DimPlot(ctrl_aggr3, reduction = "umap", label = TRUE)  
ctrl_aggr3_p1.2 <- DimPlot(ctrl_aggr3, reduction = "umap", split.by = "samples",label = TRUE)   
ctrl_aggr3_p1.3 <- DimPlot(ctrl_aggr3, reduction = "umap", group.by = "samples") 
plot_grid(ctrl_aggr3_p1.1,ctrl_aggr3_p1.2)

MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')


DefaultAssay(ctrl_aggr3) <- "RNA"
ctrl_aggr3 <- NormalizeData(ctrl_aggr3)
ctrl_aggr3_d1.1 <-DotPlot(ctrl_aggr3, features = MS_list2)+RotatedAxis()
ctrl_aggr3_d1.2 <-DotPlot(ctrl_aggr3, features = "(Gm13021)Newlnc-inter-c4-3779",split.by = "samples", cols=c("red","blue"))   
ctrl_aggr3_v1.1<- VlnPlot(ctrl_aggr3, features = "(Gm13021)Newlnc-inter-c4-3779", split.by = "samples", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)
ctrl_aggr3_r1.1<- RidgePlot(ctrl_aggr3, features = c("(Gm13021)Newlnc-inter-c4-3779","Newlnc-inter-c7-5998"), split.by = "samples", group.by = "seurat_clusters", pt.size = 0, combine = FALSE)

plot_grid(ctrl_aggr3_p1.1,ctrl_aggr3_d1.1)
plot_grid(ctrl_aggr3_p1.1,ctrl_aggr3_p1.2,ctrl_aggr3_d1.1)

######## Get some numbers and stats for avergage expression ######
table(FetchData(ctrl_aggr3, c('samples','ident')))
x <- (AverageExpression(KO.cells, verbose = TRUE, assays = "RNA" ,slot="counts")$RNA)
x["ncRNA-inter-chr7-5998",]

save(ctrl_aggr3,file ="/net/waxman-server/mnt/data/waxmanlabvm_home/kkarri/G185/G185M4M5_SCT_PC11_res0.3_GB.Rdata")

genes_hep_main =c('Alb', 'Ttr', 'Apoa1', 'Serpina1c')
genes_hep = c('Alb', 'Ttr', 'Apoa1', 'Serpina1c',
                   'Cyp2e1', 'Glul', 'Oat', 'Gulo',
                   'Ass1', 'Hamp', 'Gstp1', 'Ubb',
                   'Cyp2f2', 'Pck1', 'Hal', 'Cdh1')
genes_endo1 = c( 'Pecam1', 'Nrp1', 'Kdr','Oit3','Igfbp7','Aqp1')
genes_endo2 = c('Kdr','Oit3','Aqp1','Efnb1','Wnt2')
genes_kuppfer = c( 'Clec4f', 'Cd68')
genes_nk = c('Il2rb', 'Nkg7', 'Cxcr6', 'Gzma')
genes_b = c('Cd79a', 'Cd79b')
genes_bec = c('Epcam', 'Krt19', 'Krt7')
genes_immune = 'Ptprc'
#HSC = c("Dcn","Lama1","Nes","Ngfr")
HSC=c("Colec11","Ecm1","Hgf","Lrat","Dcn")
Dividing = "Top2a"
Bplasma= "Jchain"
Mac= "Csf1r"
Chol="Sox9"
DC=c("Irf8","Itgax")
Tcell="Cd3g"
LSEC=c('Gpr182','Fcgr2b')
New_Macrophage = c('Ccl9','Osm')

Y_genes <- c("Uty","Ddx3y","Kdm5d","Eif2s3y",   "Gm47283")
sex <- c("Cyp2b9", "ncRNA-inter-chrX-15394","Cyp2c69", 'Mup20', 'Mup1','Mup12', 'Mup21', 'Cyp2d9')
F_sex <- c('Sult3a1', 'A1bg', 'Fmo3', 'Cyp2b9', 'Sult2a1','Cyp2b13')
all_genes = c(genes_hep, genes_endo,LSEC, genes_kuppfer, Mac,genes_nk, genes_b, genes_bec, genes_immune, HSC_all,VSMC,FB, Dividing)
All_Genes = c(genes_hep_main, genes_endo2,LSEC,genes_kuppfer,Mac,genes_nk,genes_b,Bplasma,genes_bec,genes_immune,HSC,VSMC,FB,Chol,DC,Tcell,New_Macrophage)
vlnplot_genes = c('Alb','Aqp1','Efnb1','Gpr182','Fcgr2b','Wnt2','Clec4f','Csf1r','Nkg7','Cd79a','Jchain','Ngfr','Gsn','Tagln','Irf8','Cd3g','Top2a','Osm')
genes_bec_b_immune  = c(genes_bec,genes_b,genes_immune)
genes_zones = c('Cyp2e1', 'Glul', 'Oat', 'Gulo',
              'Ass1', 'Hamp', 'Gstp1', 'Ubb',
              'Cyp2f2', 'Pck1', 'Hal', 'Cdh1')

All_sex_genes <- c('Mup11','Mup7','Elovl3','Cyp4a12a','Tff3','Scara5','Mup14','Gstp2','Cyp4a12b','Mup20','Mup12','Mup21','Cyp2d9','Mup1',
                   'Sult2a5','Sult2a2','Sult2a3','Cyp3a16','Sult2a6','Cyp3a41b','Cyp3a44','Cyp3a41a','Cyp2a4','Slc22a26','Sult3a1',
'Sult2a1','Cyp2b13','A1bg','Fmo3','Cyp2c69','Cyp2b9')

#All_sex_genes <- c('Mup11|Mup7|Elovl3|Cyp4a12a|Tff3|Scara5|Mup14|Gstp2|Cyp4a12b|Mup20|Mup12|Mup21|Cyp2d9|Mup1|
                  # Sult2a5|Sult2a2|Sult2a3|Cyp3a16|Sult2a6|Cyp3a41b|Cyp3a44|Cyp3a41a|Cyp2a4|Slc22a26|Sult3a1|
#Sult2a1|Cyp2b13|A1bg|Fmo3|Cyp2c69|Cyp2b9')

All_sex_genes_all <- c(Y_genes, "(Xist)Newlnc-inter-cX-15394", All_sex_genes)
receptor_KO <- c("ncRNA-inter-chr7-5998","Cyp2b10","Nr1i2","Nr1i3","Ppara","Pparg","Ppargc1b","Ppard")
cell <- c("Stab2","Csf1r","Cd3g","Ebf1","Irf8","Sox9","Apoc3","Top2a","Dcn")
receptor_KO <- c("Nr1i2","Nr1i3","Nr1h2","Nr1h1","Nr1h3","Nr1h4","Nr1h5","Ppara","Pparg","Ppargc1b","Ppard")

################ HSC gene markers ##########################33

VSMC <- c('Tagln', 'Tpm2','Acta2','Cnn1','Pln','Myh11')
FB <- c('Gsn','Clec3b','Dpt','Cd34','Mfap4','Entpd2','Fbln2','Col15a1')
HSC_all <- c('Ecm1', 'Colec11','Vipr1','Reln','Lrat','Pth1r','Hgf','Rgs5')
HSC_PV <-  c('Ngfr','Igfbp3','Rgs4','Il34','Itgb3')
HSC_PP <- c('Loxl1','Sox4','Podn','Adamtsl2','Rspo3','Spon2') 

HSC_combined <- c(HSC_all, HSC_PV, HSC_PP,FB,VSMC )

########## endothelial gene markers #########################
Endo_PC <- c('Wnt9b', 'Rspo3', 'Cdh13', 'Wnt2')
Endo_PP <- c('Ednrb', 'Jag1', 'Lrg1', 'Efnb1', 'Ltbp4', 'Adgrg6')
LSEC <- c('Fcgr2b','Gpr182')

MS_list <- c('Kdr','Aqp1','Fcgr2b','Gpr182','Colec11','Ecm1','Lrat','Dcn','Gsn','Clec3b','Dpt','Alb','Ttr','Apoa1','Serpina1c','Clec4f','Csf1r','Tagln','Tpm2','Acta2','Irf8','Itgax','Il2rb','Nkg7','Cd3g','Cxcr6','Cd79a','Cd79b','Jchain','Ptprc','Sox9','Epcam','Krt7','Krt19','Osm','Ccl9','Top2a')


MS_list1 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Jchain','Cd79a','Cd79b','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Tagln','Tpm2','Acta2','Gsn','Clec3b','Dpt','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')

MS_list2 <- c('Sox9','Epcam','Krt7','Krt19','Alb','Ttr','Apoa1','Serpina1c','Cyp2e1','Cyp2f2','Ptprc','Irf8','Itgax','Clec4f','Csf1r','Top2a','Kdr','Aqp1','Fcgr2b','Gpr182','Dpt','Dcn','Colec11','Ecm1','Lrat','Il2rb','Nkg7','Cd3g','Cxcr6')