replace layer naming w cell resolution (cr, cell_res) + general linting

Signed-off-by: Ariel Lighty <[e-mail]>

R/genelist.R

···
 #' @format Data frames with cell type as column names.
 
 
+cr0_genelist <- read.csv(
+  file = paste0("./extdata/",
+    "ImmuneOrNot_cr0_genelist.csv",
+    sep = ""
+  ),
+  header = TRUE
+)
 
-layer0_genelist <- read.csv(file=paste("./extdata/","immune_non-immune_genelist(layer0).csv",sep=""),header=T)
-layer0_genelist
+cr1_genelist <- read.csv(file = paste0("./extdata/",
+  "MainType_cr1_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+Tcell_genelist <- read.csv(file = paste0("./extdata/",
+  "Tcell_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+Bcell_genelist <- read.csv(file = paste0("./extdata/",
+  "Bcell_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+DC_genelist <- read.csv(file = paste0("./extdata/",
+  "DC_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+NK_genelist <- read.csv(file = paste0("./extdata/",
+  "NK_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+Monocyte_genelist <- read.csv(file = paste0("./extdata/",
+  "Monocyte_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+Macrophage_genelist <- read.csv(file = paste0("./extdata/",
+  "Macrophage_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
+
+ILC_genelist <- read.csv(file = paste0("./extdata/",
+  "ILC_cr2_genelist.csv",
+  sep = ""
+), header = TRUE)
 
-layer1_genelist <- read.csv(file=paste("./extdata/","main_type_layer1_genelist.csv",sep=""),header=T)
-layer1_genelist
+CD4_genelist <- read.csv(file = paste0("./extdata/",
+  "CD4_cr3_genelist.csv",
+  sep = ""
+), header = TRUE)
 
+CD8_genelist <- read.csv(file = paste0("./extdata/",
+  "CD8_cr3_genelist.csv",
+  sep = ""
+), header = TRUE)
 
-Tcell_genelist <- read.csv(file=paste("./extdata/","Tcell_layer2_genelist.csv",sep=""),header=T)
+cr0_genelist
+cr1_genelist
 Tcell_genelist
-
-Bcell_genelist <- read.csv(file=paste("./extdata/","Bcell_layer2_genelist.csv",sep=""),header=T)
 Bcell_genelist
-
-DC_genelist <- read.csv(file=paste("./extdata/","DC_layer2_genelist.csv",sep=""),header=T)
 DC_genelist
-
-NK_genelist <- read.csv(file=paste("./extdata/","NK_layer2_genelist.csv",sep=""),header=T)
 NK_genelist
-
-Monocyte_genelist <- read.csv(file=paste("./extdata/","Monocyte_layer2_genelist.csv",sep=""),header=T)
 Monocyte_genelist
-
-Macrophage_genelist <- read.csv(file=paste("./extdata/","Macrophage_layer2_genelist.csv",sep=""),header=T)
 Macrophage_genelist
-
-ILC_genelist <- read.csv(file=paste("./extdata/","ILC_layer2_genelist.csv",sep=""),header=T)
 ILC_genelist
-
-CD4_genelist <- read.csv(file=paste("./extdata/","CD4_layer3_genelist.csv",sep=""),header=T)
 CD4_genelist
-
-CD8_genelist <- read.csv(file=paste("./extdata/","CD8_layer3_genelist.csv",sep=""),header=T)
 CD8_genelist
-
-
-# usethis::use_data(layer1_genelist)
-# usethis::use_data(layer0_genelist)
-# usethis::use_data(Tcell_genelist)
-# usethis::use_data(Bcell_genelist)
-# usethis::use_data(DC_genelist)
-# usethis::use_data(NK_genelist)
-# usethis::use_data(Monocyte_genelist)
-# usethis::use_data(Macrophage_genelist)
-# usethis::use_data(ILC_genelist)
-# usethis::use_data(CD4_genelist)
-# usethis::use_data(CD8_genelist)
-

R/scImmuCC.R

···
 #' @title scImmuCC_Layered.
-#' @description Creating Hierarchical_annotation for scRNA-Seq data immune cell.
-#' @details Input takes a cells-genes matrix with cell unique barcodes as column names and gene names as row names and returns the cells annotation.
-#' @param count  a matrix with cell unique barcodes as column names and gene names as row names .
-#' @param Non_Immune Whether non-immune cells are included in the matrix.
-#' @return Data frames with the barcodes and cell types, and some maps.
+#' @description Creating Hierarchical_annotation for scRNA-Seq data immune cell
+#' @details Input takes a cells-genes matrix with cell unique barcodes as
+#' column names and gene names as row names and returns the cells annotation
+#' @param count  Matrix with cell unique barcodes as column names and gene names
+#' as row names
+#' @param Non_Immune Whether non-immune cells are included in the matrix
+#' @return Data frames with the barcodes and cell types, and some maps
 #' @import GSVA
 #' @importFrom GSVA gsva
 #' @import Matrix
 #' @export
 #' @examples test_data
-#' library(GSVA)
-#' library(Seurat)
-
-scImmuCC_Layered <- function(count,Non_Immune=TRUE){
-
-  # if (!requireNamespace("GSVA", quietly = TRUE)) {
-  #  BiocManager::install("GSVA")
-  # }
-  # if (!requireNamespace("Seurat", quietly = TRUE)){
-  #  install.packages("Seurat")
-  # }
-  data("./data/layer1_genelist.rda",package="scImmuCC")
-  data("./data/Tcell_genelist.rda",package="scImmuCC")
-  data("./data/Bcell_genelist.rda",package="scImmuCC")
-  data("./data/DC_genelist.rda",package="scImmuCC")
-  data("./data/NK_genelist.rda",package="scImmuCC")
-  data("./data/Monocyte_genelist.rda",package="scImmuCC")
-  data("./data/Macrophage_genelist.rda",package="scImmuCC")
-  data("./data/ILC_genelist.rda",package="scImmuCC")
-  data("./data/CD4_genelist.rda",package="scImmuCC")
-  data("./data/CD8_genelist.rda",package="scImmuCC")
-  data("./data/layer0_genelist.rda",package="scImmuCC")
+scImmuCC_Layered <- function(count, Non_Immune = TRUE) {
+  data("./data/cr0_genelist.rda", package = "scImmuCC")
+  data("./data/cr1_genelist.rda", package = "scImmuCC")
+  data("./data/Tcell_genelist.rda", package = "scImmuCC")
+  data("./data/Bcell_genelist.rda", package = "scImmuCC")
+  data("./data/DC_genelist.rda", package = "scImmuCC")
+  data("./data/NK_genelist.rda", package = "scImmuCC")
+  data("./data/Monocyte_genelist.rda", package = "scImmuCC")
+  data("./data/Macrophage_genelist.rda", package = "scImmuCC")
+  data("./data/ILC_genelist.rda", package = "scImmuCC")
+  data("./data/CD4_genelist.rda", package = "scImmuCC")
+  data("./data/CD8_genelist.rda", package = "scImmuCC")
 
-
-  if(Non_Immune==TRUE){
-
-    layer0_result <- scImmuCC_main(count,layer0_genelist,"Layer0")
-    immune <- layer0_result[which(layer0_result[,2]=="Immune"),]
-    count_immune <- count[,immune[,1]]
-    ssGSEA_result <- scImmuCC_main(count_immune,layer1_genelist,"Layer1")
-    seurat_result <- seurat_Heatmap(count,layer1_genelist,ssGSEA_result,"Layer1")
-
-  }else{
-
-    ssGSEA_result <- scImmuCC_main(count,layer1_genelist,"Layer1")
-    seurat_result <- seurat_Heatmap(count,layer1_genelist,ssGSEA_result,"Layer1")
+  if (Non_Immune == TRUE) {
+    cr0_result <- scImmuCC_main(count, cr0_genelist, "cr0")
+    immune <- cr0_result[which(cr0_result[, 2] == "Immune"), ]
+    count_immune <- count[, immune[, 1]]
+    ssGSEA_result <- scImmuCC_main(count_immune, cr1_genelist, "cr1")
+    seurat_result <- seurat_Heatmap(count, cr1_genelist, ssGSEA_result, "cr1")
+  } else {
+    ssGSEA_result <- scImmuCC_main(count, cr1_genelist, "cr1")
+    seurat_result <- seurat_Heatmap(count, cr1_genelist, ssGSEA_result, "cr1")
   }
 
-  cell_type <- unique(ssGSEA_result[,2])
+  cell_type <- unique(ssGSEA_result[, 2])
 
-  if("Tcell" %in% cell_type){
-    sub_ssGSEA_Tcell <- ssGSEA_result[which(ssGSEA_result[,2]=="Tcell"),]
-    sub_count_Tcell <- count[,sub_ssGSEA_Tcell[,1]]
+  if ("Tcell" %in% cell_type) {
+    sub_ssGSEA_Tcell <- ssGSEA_result[which(ssGSEA_result[, 2] == "Tcell"), ]
+    sub_count_Tcell <- count[, sub_ssGSEA_Tcell[, 1]]
     sub_count_Tcell <- as.matrix(sub_count_Tcell)
-    ssGSEA_Tcell <- scImmuCC_main(sub_count_Tcell,Tcell_genelist,"Layer2_Tcell")
-    seurat_Tcell <- seurat_Heatmap(sub_count_Tcell,Tcell_genelist,ssGSEA_Tcell,"Layer2_Tcell")
+    ssGSEA_Tcell <- scImmuCC_main(sub_count_Tcell, Tcell_genelist, "cr2_Tcell")
+    seurat_Tcell <- seurat_Heatmap(
+      sub_count_Tcell, Tcell_genelist,
+      ssGSEA_Tcell, "cr2_Tcell"
+    )
 
-    cell_type2 <- unique(ssGSEA_Tcell[,2])
+    cell_type2 <- unique(ssGSEA_Tcell[, 2])
 
-    if("CD4_T" %in% cell_type2){
-      sub_ssGSEA_CD4 <- ssGSEA_Tcell[which(ssGSEA_Tcell[,2]=="CD4_T"),]
-      sub_count_CD4 <- count[,sub_ssGSEA_CD4[,1]]
+    if ("CD4_T" %in% cell_type2) {
+      sub_ssGSEA_CD4 <- ssGSEA_Tcell[which(ssGSEA_Tcell[, 2] == "CD4_T"), ]
+      sub_count_CD4 <- count[, sub_ssGSEA_CD4[, 1]]
       sub_count_CD4 <- as.matrix(sub_count_CD4)
-      ssGSEA_CD4 <- scImmuCC_main(sub_count_CD4,CD4_genelist,"Layer3_CD4")
-      cells <- length(sub_count_CD4[2,])
-      if(cells>50){
-        seurat_CD4 <- seurat_Heatmap(sub_count_CD4,CD4_genelist,ssGSEA_CD4,"Layer3_CD4")
+      ssGSEA_CD4 <- scImmuCC_main(sub_count_CD4, CD4_genelist, "cr3_CD4")
+      cells <- length(sub_count_CD4[2, ])
+      if (cells > 50) {
+        seurat_CD4 <- seurat_Heatmap(
+          sub_count_CD4, CD4_genelist, ssGSEA_CD4,
+          "cr3_CD4"
+        )
       }
-
     }
 
 
-    if("CD8_T" %in% cell_type2){
-      sub_ssGSEA_CD8 <- ssGSEA_Tcell[which(ssGSEA_Tcell[,2]=="CD8_T"),]
-      sub_count_CD8 <- count[,sub_ssGSEA_CD8[,1]]
+    if ("CD8_T" %in% cell_type2) {
+      sub_ssGSEA_CD8 <- ssGSEA_Tcell[which(ssGSEA_Tcell[, 2] == "CD8_T"), ]
+      sub_count_CD8 <- count[, sub_ssGSEA_CD8[, 1]]
       sub_count_CD8 <- as.matrix(sub_count_CD8)
-      ssGSEA_CD8 <- scImmuCC_main(sub_count_CD8,CD8_genelist,"Layer3_CD8")
-      cells <- length(sub_count_CD8[2,])
-      if(cells>50){
-        seurat_CD8 <- seurat_Heatmap(sub_count_CD8,CD8_genelist,ssGSEA_CD8,"Layer3_CD8")
+      ssGSEA_CD8 <- scImmuCC_main(sub_count_CD8, CD8_genelist, "cr3_CD8")
+      cells <- length(sub_count_CD8[2, ])
+      if (cells > 50) {
+        seurat_CD8 <- seurat_Heatmap(
+          sub_count_CD8, CD8_genelist, ssGSEA_CD8,
+          "cr3_CD8"
+        )
       }
-
     }
   }
 
-  if("Bcell" %in% cell_type){
-    sub_ssGSEA_Bcell <- ssGSEA_result[which(ssGSEA_result[,2]=="Bcell"),]
-    sub_count_Bcell <- count[,sub_ssGSEA_Bcell[,1]]
+  if ("Bcell" %in% cell_type) {
+    sub_ssGSEA_Bcell <- ssGSEA_result[which(ssGSEA_result[, 2] == "Bcell"), ]
+    sub_count_Bcell <- count[, sub_ssGSEA_Bcell[, 1]]
     sub_count_Bcell <- as.matrix(sub_count_Bcell)
-    ssGSEA_Bcell <- scImmuCC_main(sub_count_Bcell,Bcell_genelist,"Layer2_Bcell")
-    cells <- length(sub_count_Bcell[2,])
-    if(cells>50){
-       seurat_Bcell <- seurat_Heatmap(sub_count_Bcell,Bcell_genelist,ssGSEA_Bcell,"Layer2_Bcell")
+    ssGSEA_Bcell <- scImmuCC_main(sub_count_Bcell, Bcell_genelist, "cr2_Bcell")
+    cells <- length(sub_count_Bcell[2, ])
+    if (cells > 50) {
+      seurat_Bcell <- seurat_Heatmap(
+        sub_count_Bcell, Bcell_genelist,
+        ssGSEA_Bcell, "cr2_Bcell"
+      )
     }
   }
 
-  if("DC" %in% cell_type){
-    sub_ssGSEA_DC <- ssGSEA_result[which(ssGSEA_result[,2]=="DC"),]
-    sub_count_DC <- count[,sub_ssGSEA_DC[,1]]
+  if ("DC" %in% cell_type) {
+    sub_ssGSEA_DC <- ssGSEA_result[which(ssGSEA_result[, 2] == "DC"), ]
+    sub_count_DC <- count[, sub_ssGSEA_DC[, 1]]
     sub_count_DC <- as.matrix(sub_count_DC)
-    ssGSEA_DC <- scImmuCC_main(sub_count_DC,DC_genelist,"Layer2_DC")
-    cells <- length(sub_count_DC[2,])
-    if(cells>50){
-      seurat_DC <- seurat_Heatmap(sub_count_DC,DC_genelist,ssGSEA_DC,"Layer2_DC")
+    ssGSEA_DC <- scImmuCC_main(sub_count_DC, DC_genelist, "cr2_DC")
+    cells <- length(sub_count_DC[2, ])
+    if (cells > 50) {
+      seurat_DC <- seurat_Heatmap(
+        sub_count_DC, DC_genelist, ssGSEA_DC,
+        "cr2_DC"
+      )
     }
-
   }
 
-  if("NK" %in% cell_type){
-    sub_ssGSEA_NK <- ssGSEA_result[which(ssGSEA_result[,2]=="NK"),]
-    sub_count_NK <- count[,sub_ssGSEA_NK[,1]]
+  if ("NK" %in% cell_type) {
+    sub_ssGSEA_NK <- ssGSEA_result[which(ssGSEA_result[, 2] == "NK"), ]
+    sub_count_NK <- count[, sub_ssGSEA_NK[, 1]]
     sub_count_NK <- as.matrix(sub_count_NK)
-    ssGSEA_NK <- scImmuCC_main(sub_count_NK,NK_genelist,"Layer2_NK")
-    cells <- length(sub_count_NK[2,])
-    if(cells>50){
-      seurat_NK <- seurat_Heatmap(sub_count_NK,NK_genelist,ssGSEA_NK,"Layer2_NK")
+    ssGSEA_NK <- scImmuCC_main(sub_count_NK, NK_genelist, "cr2_NK")
+    cells <- length(sub_count_NK[2, ])
+    if (cells > 50) {
+      seurat_NK <- seurat_Heatmap(
+        sub_count_NK, NK_genelist, ssGSEA_NK,
+        "cr2_NK"
+      )
     }
   }
 
-  if("Monocyte" %in% cell_type){
-    sub_ssGSEA_Mono <- ssGSEA_result[which(ssGSEA_result[,2]=="Monocyte"),]
-    sub_count_Mono <- count[,sub_ssGSEA_Mono[,1]]
+  if ("Monocyte" %in% cell_type) {
+    sub_ssGSEA_Mono <- ssGSEA_result[which(ssGSEA_result[, 2] == "Monocyte"), ]
+    sub_count_Mono <- count[, sub_ssGSEA_Mono[, 1]]
     sub_count_Mono <- as.matrix(sub_count_Mono)
-    ssGSEA_Mono <- scImmuCC_main(sub_count_Mono,Monocyte_genelist,"Layer2_Monocyte")
-    cells <- length(sub_count_Mono[2,])
-    if(cells>50){
-      seurat_Mono <- seurat_Heatmap(sub_count_Mono,Monocyte_genelist,ssGSEA_Mono,"Layer2_Monocyte")
+    ssGSEA_Mono <- scImmuCC_main(
+      sub_count_Mono, Monocyte_genelist,
+      "cr2_Monocyte"
+    )
+    cells <- length(sub_count_Mono[2, ])
+    if (cells > 50) {
+      seurat_Mono <- seurat_Heatmap(
+        sub_count_Mono, Monocyte_genelist,
+        ssGSEA_Mono, "cr2_Monocyte"
+      )
     }
   }
 
-  if("Macrophage" %in% cell_type){
-    sub_ssGSEA_Mac <- ssGSEA_result[which(ssGSEA_result[,2]=="Macrophage"),]
-    sub_count_Mac <- count[,sub_ssGSEA_Mac[,1]]
+  if ("Macrophage" %in% cell_type) {
+    sub_ssGSEA_Mac <- ssGSEA_result[which(ssGSEA_result[, 2] == "Macrophage"), ]
+    sub_count_Mac <- count[, sub_ssGSEA_Mac[, 1]]
     sub_count_Mac <- as.matrix(sub_count_Mac)
-    ssGSEA_Mac <- scImmuCC_main(sub_count_Mac,Macrophage_genelist,"Layer2_Macrophage")
-    cells <- length(sub_count_Mac[2,])
-    if(cells>50){
-      seurat_Mac <- seurat_Heatmap(sub_count_Mac,Macrophage_genelist,ssGSEA_Mac,"Layer2_Macrophage")
+    ssGSEA_Mac <- scImmuCC_main(
+      sub_count_Mac, Macrophage_genelist,
+      "cr2_Macrophage"
+    )
+    cells <- length(sub_count_Mac[2, ])
+    if (cells > 50) {
+      seurat_Mac <- seurat_Heatmap(
+        sub_count_Mac, Macrophage_genelist,
+        ssGSEA_Mac, "cr2_Macrophage"
+      )
     }
   }
 
-  if("ILC" %in% cell_type){
-    sub_ssGSEA_ILC <- ssGSEA_result[which(ssGSEA_result[,2]=="ILC"),]
-    sub_count_ILC <- count[,sub_ssGSEA_ILC[,1]]
+  if ("ILC" %in% cell_type) {
+    sub_ssGSEA_ILC <- ssGSEA_result[which(ssGSEA_result[, 2] == "ILC"), ]
+    sub_count_ILC <- count[, sub_ssGSEA_ILC[, 1]]
     sub_count_ILC <- as.matrix(sub_count_ILC)
-    ssGSEA_ILC <- scImmuCC_main(sub_count_ILC,ILC_genelist,"Layer2_ILC")
-    cells <- length(sub_count_ILC[2,])
-    if(cells>50){
-      seurat_ILC <- seurat_Heatmap(sub_count_ILC,ILC_genelist,ssGSEA_ILC,"Layer2_ILC")
+    ssGSEA_ILC <- scImmuCC_main(sub_count_ILC, ILC_genelist, "cr2_ILC")
+    cells <- length(sub_count_ILC[2, ])
+    if (cells > 50) {
+      seurat_ILC <- seurat_Heatmap(
+        sub_count_ILC, ILC_genelist, ssGSEA_ILC,
+        "cr2_ILC"
+      )
     }
   }
 }

R/scImmuCC_main.R

···
-#' @title scImmuCC_main.
-#' @description Calculation of gene enrichment scores and annotate cell type .
-#' @details Input takes a count, genelist and filename, returns a data frame with cell annotation .
-#' @param count  a matrix with cell unique barcodes as column names and gene names as row names .
-#' @param genematrix  a data frame with cell types as column names .
-#' @param ssGSEA_result  a data frame , scImmuCC return result .
-#' @param filename  custom file name， character .
-#' @return a data frame with cell annotation.
+#' @title scImmuCC_main
+#' @description Calculation of gene enrichment scores and annotate cell type
+#' @details Input takes a count, genelist and filename, returns a data frame
+#' with cell annotation
+#' @param count Matrix with cell unique barcodes as column names and gene
+#' names as row names
+#' @param genematrix Data frame with cell types as column names
+#' @param ssGSEA_result Data frame, scImmuCC return result
+#' @param filename Custom file name, char
+#' @return Data frame with cell annotation
 #' @import GSVA
 #' @importFrom GSVA gsva
 
-
-
-scImmuCC_main <- function(count,genematrix,filename){
-
+scImmuCC_main <- function(count, genematrix, filename) {
+  genelist <- as.list(genematrix)
+  genelist <- lapply(genelist, function(x) x[!is.na(x)])
 
-  genelist <- as.list(genematrix)
-  genelist <- lapply(genelist,function(x) x[!is.na(x)])
-                     
   ssgsea <- ssgseaParam(
     count,
     genelist
-    )
-    #,
-    #assay = NA_character_,
-    #annotation = NA_character_,
-    #minSize = 1,
-    #maxSize = Inf,
-    #alpha = 0.25,
-    #normalize = TRUE
-  #)
-  
+  )
+
   ssgsea_score <- gsva(ssgsea)
-  ##ssgsea_score = gsva(count, genelist, method = "ssgsea", ssgsea.norm = TRUE, verbose = TRUE)   # signature 'matrix,list'
   score <- t(ssgsea_score)
 
   barcodes <- c()
   celltype <- c()
-  for(i in 1:length(score[,1])){
-    a1 <- score[i,]
+  for (i in 1:length(score[, 1])) {
+    a1 <- score[i, ]
     b1 <- as.data.frame(a1)
     b1$celltype <- rownames(b1)
-    f <- b1[which(b1[,1]==max(b1[,1])),]
-    barcodes <- c(barcodes,rownames(score)[i])
-    celltype <- c(celltype,rownames(f))
+    f <- b1[which(b1[, 1] == max(b1[, 1])), ]
+    barcodes <- c(barcodes, rownames(score)[i])
+    celltype <- c(celltype, rownames(f))
   }
 
-  ssGSEA_result <- data.frame(barcodes=barcodes,cell_type=celltype)
+  ssGSEA_result <- data.frame(barcodes = barcodes, cell_type = celltype)
   head(ssGSEA_result)
-  write.csv(ssGSEA_result,paste(filename,"_scImmuCC_label.csv",sep=""))
-  return(ssGSEA_result)
+  write.csv(ssGSEA_result, paste(filename, "_scImmuCC_label.csv", sep = ""))
 }

R/seurat_maps.R

···
 #' @title Seurat Object.
-#' @description Creating dynamic Seurat Object to add annotation information and draw images.
-#' @details Input takes a count, genelist and scImmuCC result, returns tsne, umap, Dotplot and pheatmaps.
-#' @param count  a matrix with cell unique barcodes as column names and gene names as row names .
-#' @param genematrix  a data frame with cell types as column names .
-#' @param ssGSEA_result  a data frame , scImmuCC1.1 return result .
-#' @param filename  custom file name， character .
-#' @return 4 pictures.
+#' @description Creating dynamic Seurat Object to add annotation information
+#' and draw images.
+#' @details Input takes a count, genelist and scImmuCC result, returns tsne,
+#' umap, Dotplot and pheatmaps.
+#' @param count  a matrix with cell unique barcodes as column names and
+#' gene names as row names
+#' @param genematrix Data frame with cell types as column names
+#' @param ssGSEA_result Data frame, scImmuCC return result
+#' @param filename Custom file name, char
+#' @return 4 pictures
 #' @import ggplot2
 #' @import Seurat
-#' @importFrom Seurat CreateSeuratObject NormalizeData FindVariableFeatures ScaleData PercentageFeatureSet VariableFeatures RunPCA RunUMAP RunTSNE DimPlot DotPlot DoHeatmap ProjectDim JackStraw ScoreJackStraw
+#' @importFrom Seurat CreateSeuratObject NormalizeData FindVariableFeatures
+#' ScaleData PercentageFeatureSet VariableFeatures RunPCA RunUMAP RunTSNE
+#' DimPlot DotPlot DoHeatmap ProjectDim JackStraw ScoreJackStraw
 
-
-
-seurat_Heatmap <- function(count,genematrix,ssGSEA_result,filename){
-
+seurat_Heatmap <- function(count, genematrix, ssGSEA_result, filename) {
   count <- count[, !duplicated(colnames(count))]
-   celltype <- intersect(colnames(count),ssGSEA_result[,1])
-  labels <- ssGSEA_result[which(ssGSEA_result[,1]%in%celltype),]
-  count <- count[,labels[,1]]
-  
-  seurat.data <- CreateSeuratObject(counts = count, project = filename)#, min.cells = 3, min.features = 200)
+  celltype <- intersect(colnames(count), ssGSEA_result[, 1])
+  labels <- ssGSEA_result[which(ssGSEA_result[, 1] %in% celltype), ]
+  count <- count[, labels[, 1]]
+
+  seurat.data <- CreateSeuratObject(counts = count, project = filename)
   seurat.data
-  seurat.data[["percent.mt"]] <- PercentageFeatureSet(seurat.data, pattern = "^MT-")
-  HB.genes_total <- c("HBA1","HBA2","HBB","HBD","HBE1","HBG1","HBG2","HBM","HBQ1","HBZ")
-  HB_m <- match(HB.genes_total,rownames(seurat.data@assays$RNA))
+  seurat.data[["percent.mt"]] <- PercentageFeatureSet(seurat.data,
+    pattern = "^MT-"
+  )
+  HB.genes_total <- c(
+    "HBA1", "HBA2", "HBB", "HBD", "HBE1", "HBG1", "HBG2",
+    "HBM", "HBQ1", "HBZ"
+  )
+  HB_m <- match(HB.genes_total, rownames(seurat.data@assays$RNA))
 
   HB.genes <- rownames(seurat.data@assays$RNA)[HB_m]
   HB.genes <- HB.genes[!is.na(HB.genes)]
-  seurat.data[["percent.HB"]]<-PercentageFeatureSet(seurat.data,features=HB.genes)
+  seurat.data[["percent.HB"]] <- PercentageFeatureSet(seurat.data,
+    features = HB.genes
+  )
 
-  seurat.data <- NormalizeData(seurat.data, normalization.method = "LogNormalize", scale.factor = 10000)
-  seurat.data <- FindVariableFeatures(seurat.data, selection.method = "vst", nfeatures = 2000)
+  seurat.data <- NormalizeData(seurat.data,
+    normalization.method = "LogNormalize",
+    scale.factor = 10000
+  )
+  seurat.data <- FindVariableFeatures(seurat.data,
+    selection.method = "vst",
+    nfeatures = 2000
+  )
   all.genes <- rownames(seurat.data)
   seurat.data <- ScaleData(seurat.data, features = all.genes)
 
-  #Perform linear dimensional reductionPerform linear dimensional reduction
-  #counts <- seurat.data[["RNA"]]@counts
+  # Perform linear dimensional reduction
   counts <- seurat.data[["RNA"]]$counts
-  cells <- length(counts[2,])
-  if(cells>50){
-    seurat.data <- RunPCA(seurat.data, features = VariableFeatures(object = seurat.data))
-  }else{
-    seurat.data <- RunPCA(seurat.data,npcs = (cells-1), features = VariableFeatures(object = seurat.data))
+  cells <- length(counts[2, ])
+  if (cells > 50) {
+    seurat.data <- RunPCA(seurat.data,
+      features = VariableFeatures(object = seurat.data)
+    )
+  } else {
+    seurat.data <- RunPCA(seurat.data,
+      npcs = (cells - 1),
+      features = VariableFeatures(object = seurat.data)
+    )
   }
-  
+
   head(seurat.data@reductions$[e-mail])
   head(seurat.data@reductions$[e-mail])
   seurat.data <- ProjectDim(object = seurat.data)
   seurat.data <- JackStraw(seurat.data, num.replicate = 100)
   seurat.data <- ScoreJackStraw(seurat.data, dims = 1:20)
-  #seurat.data <- JackStraw(seurat.data, dims = 50)
-  #seurat.data <- ScoreJackStraw(seurat.data, dims = 1:50)
 
   seurat.data <- RunUMAP(seurat.data, dims = 1:10)
-  n <- length(count[2,])
-  #seurat.data <- seurat.data[ ,!duplicated(colnames(seurat.data))]
-  
-   min_cell_count <- 100  
-  if(ncol(seurat.data) >= min_cell_count){
-    seurat.data <- RunTSNE(seurat.data, dims = 1:10,check_duplicates = FALSE )
-  }
-  else{
-    seurat.data <- RunTSNE(seurat.data, dims = 1:10, perplexity = 5,check_duplicates = FALSE )
+  n <- length(count[2, ])
+
+  min_cell_count <- 100
+  if (ncol(seurat.data) >= min_cell_count) {
+    seurat.data <- RunTSNE(seurat.data, dims = 1:10, check_duplicates = FALSE)
+  } else {
+    seurat.data <- RunTSNE(seurat.data,
+      dims = 1:10, perplexity = 5,
+      check_duplicates = FALSE
+    )
   }
 
   head(seurat.data@reductions$[e-mail])
 
-  #Add annotation information to Seurat object
-  [e-mail]$cell_type_pred <-labels[,2]
+  # Add annotation information to Seurat object
+  [e-mail]$cell_type_pred <- labels[, 2]
 
-  pdf(paste(filename, "_tSNE", ".pdf", sep=""), width=12, height=10)
-  p1 <- DimPlot(seurat.data, reduction = "tsne", group.by = "cell_type_pred",label = TRUE, pt.size=1)
+  pdf(paste(filename, "_tSNE", ".pdf", sep = ""), width = 12, height = 10)
+  p1 <- DimPlot(seurat.data,
+    reduction = "tsne",
+    group.by = "cell_type_pred", label = TRUE, pt.size = 1
+  )
   plot(p1)
   dev.off()
-  pdf(paste(filename, "_UMAP", ".pdf", sep=""), width=12, height=10)
-  p2 <- DimPlot(seurat.data, reduction = "umap", group.by = "cell_type_pred",label = TRUE, pt.size=1)
+  pdf(paste(filename, "_UMAP", ".pdf", sep = ""), width = 12, height = 10)
+  p2 <- DimPlot(seurat.data,
+    reduction = "umap",
+    group.by = "cell_type_pred", label = TRUE, pt.size = 1
+  )
   plot(p2)
   dev.off()
 
   genelist <- as.list(genematrix)
-  genelist <- lapply(genelist,function(x) x[!is.na(x)])
+  genelist <- lapply(genelist, function(x) x[!is.na(x)])
   gene <- c()
-  for(i in 1:length(genelist)){
-    gene <- c(gene,genelist[[i]])
+  for (i in 1:length(genelist)) {
+    gene <- c(gene, genelist[[i]])
   }
+
   length(gene)
   gene <- unique(gene)
-  Y <- intersect(gene,rownames(count))
+  Y <- intersect(gene, rownames(count))
   length(Y)
   features <- Y
-  pdf(paste(filename,"_UMAP_DotPlot",".pdf",sep=""), width=12, height=10)
-  p3 <- DotPlot(seurat.data, features = features,group.by = "cell_type_pred") + RotatedAxis()
+  pdf(paste(filename, "_UMAP_DotPlot", ".pdf", sep = ""),
+    width = 12,
+    height = 10
+  )
+  p3 <- DotPlot(seurat.data,
+    features = features,
+    group.by = "cell_type_pred"
+  ) + RotatedAxis()
   plot(p3)
   dev.off()
 
-  pdf(paste(filename, "_DoHeatmap.pdf", sep=""), width=12, height=10)
-  p4 <- DoHeatmap(seurat.data, features = Y,group.by = "cell_type_pred") + NoLegend()
+  pdf(paste(filename, "_DoHeatmap.pdf", sep = ""), width = 12, height = 10)
+  p4 <- DoHeatmap(seurat.data,
+    features = Y,
+    group.by = "cell_type_pred"
+  ) + NoLegend()
   plot(p4)
   dev.off()
-
 }
-

extdata/Bcell_layer2_genelist.csv extdata/Bcell_cr2_genelist.csv

···
-Naive_B,Memory_B,Plasma_Cell
-TCL1A,MS4A1,TNFRSF17
-FCER2,LINC00926,GPRC5D
-LINC00926,BANK1,DERL3
-FCER2,CD79A,IGF1
-MS4A1,HLA-DQB1,GLDC
-CD79A,FCER2,HRASLS2
-BANK1,HLA-DQA1,SPACA3
-FCRL1,CD22,SDC1
-HLA-DQB1,FCRL2,MZB1
-HVCN1,ADAM28,TXNDC5
-HLA-DQA1,IGLL5,IgG
-,P2RX5,IGHG4
-,BLK,IGHG1
-,,IGHG3
-,,IGKC
-,,SSR4
-,,FKBP11
-,,AREG
-,,IGLC2
-,,KLRD1
+Naive_B,Memory_B,Plasma_Cell
+TCL1A,MS4A1,TNFRSF17
+FCER2,LINC00926,GPRC5D
+LINC00926,BANK1,DERL3
+FCER2,CD79A,IGF1
+MS4A1,HLA-DQB1,GLDC
+CD79A,FCER2,HRASLS2
+BANK1,HLA-DQA1,SPACA3
+FCRL1,CD22,SDC1
+HLA-DQB1,FCRL2,MZB1
+HVCN1,ADAM28,TXNDC5
+HLA-DQA1,IGLL5,IgG
+,P2RX5,IGHG4
+,BLK,IGHG1
+,,IGHG3
+,,IGKC
+,,SSR4
+,,FKBP11
+,,AREG
+,,IGLC2
+,,KLRD1

extdata/CD4_layer3_genelist.csv extdata/CD4_cr3_genelist.csv

···
-CD4_naive,Treg,Th1,Th2,Th17,Tfh,CD4_Central_memory,CD4_Effector_memory
-TCF7,FOXP3,CD28,ADORA2A,CD274,CD200,MALT1,GFI1
-CCR7,CTLA4,CD274,APOE,IL10RA,CD83,PDK1,MALT1
-CD62L,CD25,IL2,C5AR1,PDCD1,CD84,SKIV2L,SERPINB6
-CD127,GITR,IL4,IL13RA1,IL6,B3GAT1,CCR7,LRRC32
-CD45RA,TIGIT,IL6,MMP9,IL17A,CDK5R1,IL23A,PDK1
-CD132,IL10,IL10RA,MYD88,IL17AF,IL21,CD27,SKIV2L
-IKBKE,AITR,IL12,PTPN13,IL17F,CXCR5,CD28,SPNS2
-LAMP3,LAG3,IL23A,SPI1,IL21,IL4,CD45RO,SSU72
-TRABD2A,LTA,MYD88,TBX21,IL22,STAT3,CD62L,TREX1
-LEF1,IL2RA,PDCD1,TLR4,IL23R,BCL6,CD44,CXCL14
-BCL11B,ITGAE,PLP1,STAT6,AHR,c-MAF,IL7R,NLRP3
-LDLRAP1,GARPh,PTPN2,TIGIT,BATF,BTLA,CD127,TNFRSF4
-,LAPh,TGFB1,TNFRSF4,IL21R,CXCL13,TNFRSF25,SSU72
-,CCR8,CASP8,TSLP,CCR6,B3GAT1,ETS1,TREX1
-,TGFBR1,CRP,USP38,RORA,CD57,AKT3,CD127
-,IL9,IL27RA,GATA3,RORγt,IRF4,ITK,KLRG1
-,SKP2,ICAM1,IRF4,RORC,BATF,TNFSF8,KRG1
-,IL2,IL12B,IL4,IL1R,SLAM,SF3A3,IL12RB1
-,IL10R,TBET,IL5,CTSH,OX4OL,ZNF143,TNFSF4
-,TSC1,IL12R,IL13,IL4I1,CD40L,SMAD3,GPATCH3
-,RICTOR,CXCR3,IL25R,LGALS3,CMAF,CNOT4,PRPF38A
-,,IL18,IL33R,,CD200,CCR4,ITCH
-,,STAT1,BATF,,GNG4,SESN3,IL23R
-,,STAT4,GATA-3,,POU2AF1,TRAT1,ATM
-,,IFNG,STAT6,,IGFBP4,,PNMA1
-,,IL3,CRTH2,,,,RORA
-,,TNF,IL17RB,,,,BMI1
-,,CSF2,ST2,,,,DLL1
-,,,TIM1,,,,FCF1
-,,,CCR4,,,,ATG5
-,,,CCR8,,,,
-,,,CRTH2,,,,
-,,,PTGDR2,,,,
-,,,SEMA5A,,,,
-,,,AKAP12,,,,
-,,,HPGDS,,,,
+CD4_naive,Treg,Th1,Th2,Th17,Tfh,CD4_Central_memory,CD4_Effector_memory
+TCF7,FOXP3,CD28,ADORA2A,CD274,CD200,MALT1,GFI1
+CCR7,CTLA4,CD274,APOE,IL10RA,CD83,PDK1,MALT1
+CD62L,CD25,IL2,C5AR1,PDCD1,CD84,SKIV2L,SERPINB6
+CD127,GITR,IL4,IL13RA1,IL6,B3GAT1,CCR7,LRRC32
+CD45RA,TIGIT,IL6,MMP9,IL17A,CDK5R1,IL23A,PDK1
+CD132,IL10,IL10RA,MYD88,IL17AF,IL21,CD27,SKIV2L
+IKBKE,AITR,IL12,PTPN13,IL17F,CXCR5,CD28,SPNS2
+LAMP3,LAG3,IL23A,SPI1,IL21,IL4,CD45RO,SSU72
+TRABD2A,LTA,MYD88,TBX21,IL22,STAT3,CD62L,TREX1
+LEF1,IL2RA,PDCD1,TLR4,IL23R,BCL6,CD44,CXCL14
+BCL11B,ITGAE,PLP1,STAT6,AHR,c-MAF,IL7R,NLRP3
+LDLRAP1,GARPh,PTPN2,TIGIT,BATF,BTLA,CD127,TNFRSF4
+,LAPh,TGFB1,TNFRSF4,IL21R,CXCL13,TNFRSF25,SSU72
+,CCR8,CASP8,TSLP,CCR6,B3GAT1,ETS1,TREX1
+,TGFBR1,CRP,USP38,RORA,CD57,AKT3,CD127
+,IL9,IL27RA,GATA3,RORγt,IRF4,ITK,KLRG1
+,SKP2,ICAM1,IRF4,RORC,BATF,TNFSF8,KRG1
+,IL2,IL12B,IL4,IL1R,SLAM,SF3A3,IL12RB1
+,IL10R,TBET,IL5,CTSH,OX4OL,ZNF143,TNFSF4
+,TSC1,IL12R,IL13,IL4I1,CD40L,SMAD3,GPATCH3
+,RICTOR,CXCR3,IL25R,LGALS3,CMAF,CNOT4,PRPF38A
+,,IL18,IL33R,,CD200,CCR4,ITCH
+,,STAT1,BATF,,GNG4,SESN3,IL23R
+,,STAT4,GATA-3,,POU2AF1,TRAT1,ATM
+,,IFNG,STAT6,,IGFBP4,,PNMA1
+,,IL3,CRTH2,,,,RORA
+,,TNF,IL17RB,,,,BMI1
+,,CSF2,ST2,,,,DLL1
+,,,TIM1,,,,FCF1
+,,,CCR4,,,,ATG5
+,,,CCR8,,,,
+,,,CRTH2,,,,
+,,,PTGDR2,,,,
+,,,SEMA5A,,,,
+,,,AKAP12,,,,
+,,,HPGDS,,,,

extdata/CD8_layer3_genelist.csv extdata/CD8_cr3_genelist.csv

···
-CD8_naive,CD8_Cytotoxic,CD8_Exhausted,CD8_Central_memory,CD8_Effector_memory
-NELL2,AP20187,LAG3,MALT1,GFI1
-REG4,ERBB2,PD1,IDO1,IL23R
-CD248,GFI1,TIM3,SKIV2L,MALT1
-MGAT4A,IL18,2B4,TRAF2,JAK3
-NELL2,IL23R,CD200,SLC2A1,PDCD1LG2
-CCR7,MALT1,TOX2,CD40LG,SELPLG
-LEF1,MUC1,TOX,GZMK,SPNS2
-APBA2,TERT,PRDM1,CCL5,BRD4
-CARS,ADIPOQ,RBPJ,DUSP2,C1QBP
-PIK3IP1,CD274,PDCD1,LYAR,ITPR2
-,INS,TIGIT,ZNF683,NLRP3
-,IPMK,HAVCR2,IL32,TRAF2
-,PRF1,ZNF683,CXCR3,GZMK
-,GZMA,LAYN,IL7R,DUSP2
-,GZMB,ITGAE,TYROBP,CCL5
-,GZMK,ANO6,,CST7
-,NKG7,CDX-1127,,GZMA
-,GNLY,IL2RB,,CMC1
-,CX3CR1,MALT1,,CCL4
-,KLRG1,NR1H4,,GZMM
-,FGFBP2,TNFRSF18,,
-,FCGR3A,VTCN1,,
+CD8_naive,CD8_Cytotoxic,CD8_Exhausted,CD8_Central_memory,CD8_Effector_memory
+NELL2,AP20187,LAG3,MALT1,GFI1
+REG4,ERBB2,PD1,IDO1,IL23R
+CD248,GFI1,TIM3,SKIV2L,MALT1
+MGAT4A,IL18,2B4,TRAF2,JAK3
+NELL2,IL23R,CD200,SLC2A1,PDCD1LG2
+CCR7,MALT1,TOX2,CD40LG,SELPLG
+LEF1,MUC1,TOX,GZMK,SPNS2
+APBA2,TERT,PRDM1,CCL5,BRD4
+CARS,ADIPOQ,RBPJ,DUSP2,C1QBP
+PIK3IP1,CD274,PDCD1,LYAR,ITPR2
+,INS,TIGIT,ZNF683,NLRP3
+,IPMK,HAVCR2,IL32,TRAF2
+,PRF1,ZNF683,CXCR3,GZMK
+,GZMA,LAYN,IL7R,DUSP2
+,GZMB,ITGAE,TYROBP,CCL5
+,GZMK,ANO6,,CST7
+,NKG7,CDX-1127,,GZMA
+,GNLY,IL2RB,,CMC1
+,CX3CR1,MALT1,,CCL4
+,KLRG1,NR1H4,,GZMM
+,FGFBP2,TNFRSF18,,
+,FCGR3A,VTCN1,,

extdata/DC_layer2_genelist.csv extdata/DC_cr2_genelist.csv

···
-cDC,pDC
-ENHO,LILRA4
-FCER1A,CLEC4C
-IGSF6,IL3RA
-CTSV,SMPD3
-ANXA2,SERPINF1
-H2AFZ,PLD4
-LGALS1,PTPRS
-HLA-DPA1,SCT
-CLSPN,PTCRA
-TUBA1B,IRF7
-CLEC10A,IRF8
-CD1C,CCDC50
-PKIB,TCF4
-CD1E,UGCG
-HLA-DQA1,ITM2C
-HLA-DQB1,LGMN
-HLA-DRB5,LILRA4
-CPVL,APP
+cDC,pDC
+ENHO,LILRA4
+FCER1A,CLEC4C
+IGSF6,IL3RA
+CTSV,SMPD3
+ANXA2,SERPINF1
+H2AFZ,PLD4
+LGALS1,PTPRS
+HLA-DPA1,SCT
+CLSPN,PTCRA
+TUBA1B,IRF7
+CLEC10A,IRF8
+CD1C,CCDC50
+PKIB,TCF4
+CD1E,UGCG
+HLA-DQA1,ITM2C
+HLA-DQB1,LGMN
+HLA-DRB5,LILRA4
+CPVL,APP

extdata/ILC_layer2_genelist.csv extdata/ILC_cr2_genelist.csv

···
-ILC1,ILC2,ILC3
-SYNE2,GNLY,KIT
-CCL5,KLRD1,KLRB1
-HSPA1B,CCL4,KLRD1
-CD3E,CMC,GZMK
-FYB1,NKG7,GZMH
-TRAT1,IL5,CXCL8
-DNAJB1,IL13,CD83
-TBX21,IL1RL1,LST1
-EOMES,PDE4A,IL1R1
-CXCR3,RORA,CXCL2
-IL12RB1,BCL11B,ATP1B3
-IL12RB2,PTGDR2,NRIP1
-IL18RAP,KLRG1,VEGFA
-IL18R1,IL17RB,NCOA7
-KLRD1,CRLF2,RORC
-KLRC1,,NFIL3
-KLRF1,,AHR
-,,IL23R
-,,CSF2
-,,TNF
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
-,,
+ILC1,ILC2,ILC3
+SYNE2,GNLY,KIT
+CCL5,KLRD1,KLRB1
+HSPA1B,CCL4,KLRD1
+CD3E,CMC,GZMK
+FYB1,NKG7,GZMH
+TRAT1,IL5,CXCL8
+DNAJB1,IL13,CD83
+TBX21,IL1RL1,LST1
+EOMES,PDE4A,IL1R1
+CXCR3,RORA,CXCL2
+IL12RB1,BCL11B,ATP1B3
+IL12RB2,PTGDR2,NRIP1
+IL18RAP,KLRG1,VEGFA
+IL18R1,IL17RB,NCOA7
+KLRD1,CRLF2,RORC
+KLRC1,,NFIL3
+KLRF1,,AHR
+,,IL23R
+,,CSF2
+,,TNF
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,
+,,

extdata/Macrophage_layer2_genelist.csv extdata/Macrophage_cr2_genelist.csv

···
-Macrophage_M1,Macrophage_M2
-iNOS,ARG1
-IL12,ARG2
-CD64,FCER2
-CD80,IL10
-CXCR10,CD32
-IL23,CD163
-CXCL9,CD23
-CXCL10,CD200R1
-CXCL11,PD-L2
-CD86,PD-L1
-IL1A,MARCO
-IL1B,CSF1R
-IL6,CD206
-TNFa,Il1RA
-MHCII,Il1R2
-CCL5,IL4R
-IRF5,CCL4
-IRF1,CCL13
-CD40,CCL20
-IDO1,CCL17
-KYNU,CCL18
-CCR7,CCL22
-B7-1,CCL24
-B7-2,LYVE1
-,VEGFA
-,VEGFB
-,VEGFC
-,VEGFD
-,EGF
-,CTSA
-,CTSB
-,CSTC
-,CTSD
-,TGFB1
-,TGFB2
-,TGFB3
-,MMP14
-,MMP19
-,MMP9
-,CLEC7A
-,WNT7b
-,FASL
-,TNFSF12
-,TNFSF8
-,CD276
-,VTCN1
-,MSR1
-,FN1
-,IRF4
-,HLA-DR
-,CD205
-,CD14
-,PDL2
-,PDL1
-,PDCD1LG2
-,CD274
-,MRC1
-,IL1RN
-,B7-H3
-,BH-H4
-,CD204
+Macrophage_M1,Macrophage_M2
+iNOS,ARG1
+IL12,ARG2
+CD64,FCER2
+CD80,IL10
+CXCR10,CD32
+IL23,CD163
+CXCL9,CD23
+CXCL10,CD200R1
+CXCL11,PD-L2
+CD86,PD-L1
+IL1A,MARCO
+IL1B,CSF1R
+IL6,CD206
+TNFa,Il1RA
+MHCII,Il1R2
+CCL5,IL4R
+IRF5,CCL4
+IRF1,CCL13
+CD40,CCL20
+IDO1,CCL17
+KYNU,CCL18
+CCR7,CCL22
+B7-1,CCL24
+B7-2,LYVE1
+,VEGFA
+,VEGFB
+,VEGFC
+,VEGFD
+,EGF
+,CTSA
+,CTSB
+,CSTC
+,CTSD
+,TGFB1
+,TGFB2
+,TGFB3
+,MMP14
+,MMP19
+,MMP9
+,CLEC7A
+,WNT7b
+,FASL
+,TNFSF12
+,TNFSF8
+,CD276
+,VTCN1
+,MSR1
+,FN1
+,IRF4
+,HLA-DR
+,CD205
+,CD14
+,PDL2
+,PDL1
+,PDCD1LG2
+,CD274
+,MRC1
+,IL1RN
+,B7-H3
+,BH-H4
+,CD204

extdata/Monocyte_layer2_genelist.csv extdata/Monocyte_cr2_genelist.csv

···
-Classical_Mono,NonClassical_Mono
-CD14,CD16
-VCAN,C1QA
-CD36,CDKN1C
-FOSL2,HES4
-S100A12,MS4A7
-PLAUR,FCGR3A
-LGALS2,TCF7L2
-MNDA,HMOX1
-SLC11A1,LYPD2
-,IFITM3
-,CSF1R
+Classical_Mono,NonClassical_Mono
+CD14,CD16
+VCAN,C1QA
+CD36,CDKN1C
+FOSL2,HES4
+S100A12,MS4A7
+PLAUR,FCGR3A
+LGALS2,TCF7L2
+MNDA,HMOX1
+SLC11A1,LYPD2
+,IFITM3
+,CSF1R

extdata/NK_layer2_genelist.csv extdata/NK_cr2_genelist.csv

···
-NK_bright,NK_dim
-CD56,CD16
-ARH,ENC1
-BMP2,TTC38
-COL9A2,PRF1
-IL7R,CMKLR1
-GZMK,BOK
-JAML,CX3CR1
-CCDC141,DGKK
-ITM2C,SLC1A7
-XCL1,MYOM2
-SYPL1,MTSS1
-SETD7,FGFBP2
-RCAN3,LGR6
-KIT,KIR2DL1
-SPRY2,CD298
-,ATP1B3
-,ADGRG1
-,TGFBR3
-,SPON2
-,ZEB2
-,BNC2
-,EBF4
-,ASCL2
-,TBX21
+NK_bright,NK_dim
+CD56,CD16
+ARH,ENC1
+BMP2,TTC38
+COL9A2,PRF1
+IL7R,CMKLR1
+GZMK,BOK
+JAML,CX3CR1
+CCDC141,DGKK
+ITM2C,SLC1A7
+XCL1,MYOM2
+SYPL1,MTSS1
+SETD7,FGFBP2
+RCAN3,LGR6
+KIT,KIR2DL1
+SPRY2,CD298
+,ATP1B3
+,ADGRG1
+,TGFBR3
+,SPON2
+,ZEB2
+,BNC2
+,EBF4
+,ASCL2
+,TBX21

extdata/Tcell_layer2_genelist.csv extdata/Tcell_cr2_genelist.csv

···
-CD4_T,CD8_T
-CD4,CD8A
-TRDC,CD8B
-IL2RA,GZMB
-CD25,EOMES
-STAT1,TNFA
-IL2,IFNG
-CD127,CCL5
-CD152,NKG7
-CTLA4,GZMH
-CTLA-4,GZMK
-FOXP3,GZMA
-IL4,CD8
-IL10,
-IL12,
-TGFB,
-IL18,
-STAT5,
-CD4,
+CD4_T,CD8_T
+CD4,CD8A
+TRDC,CD8B
+IL2RA,GZMB
+CD25,EOMES
+STAT1,TNFA
+IL2,IFNG
+CD127,CCL5
+CD152,NKG7
+CTLA4,GZMH
+CTLA-4,GZMK
+FOXP3,GZMA
+IL4,CD8
+IL10,
+IL12,
+TGFB,
+IL18,
+STAT5,
+CD4,

extdata/immune_non-immune_genelist(layer0).csv extdata/ImmuneOrNot_cr0_genelist.csv

···
-Immune,Non_Immune
-PTPRC,MUC5A
-CD45,KRT5
-CSF3R,SFTPD
-FCGR3B,EPCAM
-KLRD1,CDH5
-FPR1,COL1A2
-CD8A,ACTA2
-CD1B,PECAM1
-TNFRSF17,COL3A1
-BANK1,TMSB10
-FCRL2,CALD1
-PNOC,FTH1
-CR2,COL6A2
-FCN1,FKBP1A
-GNLY,
-KIR2DL1,
-KIR3DL1,
-KIR3DL2,
-CD1E,
-CD1A,
-CD163,
-AIF1,
-CD79A,
-JCHAIN,
+Immune,Non_Immune
+PTPRC,MUC5A
+CD45,KRT5
+CSF3R,SFTPD
+FCGR3B,EPCAM
+KLRD1,CDH5
+FPR1,COL1A2
+CD8A,ACTA2
+CD1B,PECAM1
+TNFRSF17,COL3A1
+BANK1,TMSB10
+FCRL2,CALD1
+PNOC,FTH1
+CR2,COL6A2
+FCN1,FKBP1A
+GNLY,
+KIR2DL1,
+KIR3DL1,
+KIR3DL2,
+CD1E,
+CD1A,
+CD163,
+AIF1,
+CD79A,
+JCHAIN,

extdata/main_type_layer1_genelist.csv extdata/MainTypes_cr1_genelist.csv

···
-Tcell,Bcell,DC,NK,Monocyte,Macrophage,ILC,Mast,Neutrophil
-CD3D,CD79A,IL3RA,GNLY,VCAN,APOE,IRF8,TPSB2,CSF3R
-CD3G,CD79B,CD1C,KLRC1,CSF3R,APOC1,PLD4,TPSAB1,FCGR3B
-CD3E,MS4A1,BATF3,FGFBP2,CDKN1C,C1QB,IRF7,TPSD1,FPR1
-CD4,CD19,THBD,KLRF1,LRRC25,C1QC,LILRA4,TESPA1,CEACAM3
-CD8A,CD20,CD209,GZMH,TCF7L2,RNASE1,TCF4,RGS13,TNFRSF10C
-CD8B,AFF3,HLA-DPB1,PRF1,LYN,ACP5,SERPINF1,SLC18A2,PLXNC1
-FOXP3,BANK1,HLA-DQA1,KLRD1,PILRA,GPNMB,CCDC50,CPA3,FCAR
-IL2RA,BLK,CSF2RA,NKG7,FCGR1A,PLD3,UGCG,MS4A2,ABTB1
-IL7R,BTLA,HLA-DRA,GZMB,CTSS,CTSB,APP,HPGDS,SLC25A37
-TRAC,CD138,HLA-DPA1,SPON2,FCN1,PLTP,PPP1R14B,ADCYAP1,CXCR1
-CTLA4,MZB1,HLA-DRB1,CD56,VCAN,CD9,IRF4,HDC,BCL6
-DFNB31,IGHM,HLA-DQB1,,CSF3R,PRDX1,TSPAN13,FCER1A,NCF2
-EOMES,IGHG3,LDLRAD4,,LYZ,CTSZ,SPIB,GATA2,CXCR2
-TIM3,IGHA2,HLA-DRB5,,FCN1,DAB2,TPM2,KIT,MGAM
-,,RALA,,CTSS,CD63,LILRB4,,FAM65B
-,,HLA-DRB6,,CD14,CD81,LRRC26,,THBD
-,,AXL,,SA00A9,LIPA,BCL11A,,LILRA2
-,,CD86,,IF130,GLUL,SOX4,,CAMP
-,,HLA-DMB,,AIF1,SLCO2B1,KRT86,,TNFSF14
-,,FCER1A,,,CREG1,KRT81,,SEPX1
-,,HBB,,,LGALS3,IL23R,,XPO6
-,,CPVL,,,LAMP1,PCDH9,,CD97
-,,CD74,,,MARCO,DLL1,,TNFRSF1A
-,,CST3,,,FABP4,RORC,,
-,,CD123,,,MCEMP1,PDZK1,,
-,,CD103,,,MERTK,TNFSF11,,
-,,CD1E,,,CD163,ENPP1,,
-,,CD1A,,,FCGR3A,B3GALT5,,
-,,CLEC10A,,,LGMN,NCR2,,
-,,SIGLEC1,,,PLA2G7,LIF,,
-,,UBD,,,CSF1R,IL4I1,,
-,,STAB1,,,CYBB,SLC4A10,,
-,,CSF2RA,,,CD163,HOXA9,,
-,,GPR109B,,,NPL,CSF2,,
-,,SIGLEC5,,,FN1,TLE1,,
-,,,,,IL1B,TNFSF4,,
-,,,,,S100A8,IL1R1,,
-,,,,,MS4A6A,B3GNT7,,
-,,,,,FCER1Q,AMFR,,
-,,,,,,HIF3A,,
-,,,,,,MTRNR2L6,,
-,,,,,,MTRNR2L10,,
-,,,,,,LTB,,
-,,,,,,MTRNR2L1,,
+Tcell,Bcell,DC,NK,Monocyte,Macrophage,ILC,Mast,Neutrophil
+CD3D,CD79A,IL3RA,GNLY,VCAN,APOE,IRF8,TPSB2,CSF3R
+CD3G,CD79B,CD1C,KLRC1,CSF3R,APOC1,PLD4,TPSAB1,FCGR3B
+CD3E,MS4A1,BATF3,FGFBP2,CDKN1C,C1QB,IRF7,TPSD1,FPR1
+CD4,CD19,THBD,KLRF1,LRRC25,C1QC,LILRA4,TESPA1,CEACAM3
+CD8A,CD20,CD209,GZMH,TCF7L2,RNASE1,TCF4,RGS13,TNFRSF10C
+CD8B,AFF3,HLA-DPB1,PRF1,LYN,ACP5,SERPINF1,SLC18A2,PLXNC1
+FOXP3,BANK1,HLA-DQA1,KLRD1,PILRA,GPNMB,CCDC50,CPA3,FCAR
+IL2RA,BLK,CSF2RA,NKG7,FCGR1A,PLD3,UGCG,MS4A2,ABTB1
+IL7R,BTLA,HLA-DRA,GZMB,CTSS,CTSB,APP,HPGDS,SLC25A37
+TRAC,CD138,HLA-DPA1,SPON2,FCN1,PLTP,PPP1R14B,ADCYAP1,CXCR1
+CTLA4,MZB1,HLA-DRB1,CD56,VCAN,CD9,IRF4,HDC,BCL6
+DFNB31,IGHM,HLA-DQB1,,CSF3R,PRDX1,TSPAN13,FCER1A,NCF2
+EOMES,IGHG3,LDLRAD4,,LYZ,CTSZ,SPIB,GATA2,CXCR2
+TIM3,IGHA2,HLA-DRB5,,FCN1,DAB2,TPM2,KIT,MGAM
+,,RALA,,CTSS,CD63,LILRB4,,FAM65B
+,,HLA-DRB6,,CD14,CD81,LRRC26,,THBD
+,,AXL,,SA00A9,LIPA,BCL11A,,LILRA2
+,,CD86,,IF130,GLUL,SOX4,,CAMP
+,,HLA-DMB,,AIF1,SLCO2B1,KRT86,,TNFSF14
+,,FCER1A,,,CREG1,KRT81,,SEPX1
+,,HBB,,,LGALS3,IL23R,,XPO6
+,,CPVL,,,LAMP1,PCDH9,,CD97
+,,CD74,,,MARCO,DLL1,,TNFRSF1A
+,,CST3,,,FABP4,RORC,,
+,,CD123,,,MCEMP1,PDZK1,,
+,,CD103,,,MERTK,TNFSF11,,
+,,CD1E,,,CD163,ENPP1,,
+,,CD1A,,,FCGR3A,B3GALT5,,
+,,CLEC10A,,,LGMN,NCR2,,
+,,SIGLEC1,,,PLA2G7,LIF,,
+,,UBD,,,CSF1R,IL4I1,,
+,,STAB1,,,CYBB,SLC4A10,,
+,,CSF2RA,,,CD163,HOXA9,,
+,,GPR109B,,,NPL,CSF2,,
+,,SIGLEC5,,,FN1,TLE1,,
+,,,,,IL1B,TNFSF4,,
+,,,,,S100A8,IL1R1,,
+,,,,,MS4A6A,B3GNT7,,
+,,,,,FCER1Q,AMFR,,
+,,,,,,HIF3A,,
+,,,,,,MTRNR2L6,,
+,,,,,,MTRNR2L10,,
+,,,,,,LTB,,
+,,,,,,MTRNR2L1,,