单细胞数据挖掘实战：文献复现（二）批量创建Seurat对象及质控
复现Figure 2a左边那张图
一、加载R包
if(T){
if(!require(BiocManager))install.packages("BiocManager")
if(!require(Seurat))install.packages("Seurat")
if(!require(Matrix))install.packages("Matrix")
if(!require(ggplot2))install.packages("ggplot2")
if(!require(cowplot))install.packages("cowplot")
if(!require(magrittr))install.packages("magrittr")
if(!require(dplyr))install.packages("dplyr")
if(!require(purrr))install.packages("purrr")
if(!require(ggrepel))install.packages("ggrepel")
if(!require(ggpubr))install.packages("ggpubr")
}
二、数据处理
#挑选MG, Mo/MΦ,BAM三个细胞簇
Idents(sex_condition_objects[[1]]) <- sex_condition_objects[[1]]@meta.data$cell_type_selection
table(Idents(sex_condition_objects[[1]]))
#Microglia BAM
# 9454 500
clusters_taken_1 <- subset(sex_condition_objects[[1]], idents = c("Microglia","BAM"))
Idents(sex_condition_objects[[2]]) <- sex_condition_objects[[2]]@meta.data$cell_type_selection
table(Idents(sex_condition_objects[[2]]))
#Macrophages Microglia BAM
# 2131 1658 6981 375
clusters_taken_2 <- subset(sex_condition_objects[[2]], idents = c("Microglia","Macrophages","BAM"))
Idents(sex_condition_objects[[3]]) <- sex_condition_objects[[3]]@meta.data$cell_type_selection
table(Idents(sex_condition_objects[[3]]))
#Microglia BAM
# 9078 619 362
clusters_taken_3 <- subset(sex_condition_objects[[3]], idents = c("Microglia","BAM"))
Idents(sex_condition_objects[[4]]) <- sex_condition_objects[[4]]@meta.data$cell_type_selection
table(Idents(sex_condition_objects[[4]]))
#Macrophages Microglia BAM
# 2301 6071 527 344
clusters_taken_4 <- subset(sex_condition_objects[[4]], idents = c("Microglia","Macrophages","BAM"))
clusters_taken_list <- c(clusters_taken_1,clusters_taken_2,clusters_taken_3,
clusters_taken_4)
names(clusters_taken_list) <- names(sex_condition_objects)
# Normalize
clusters_objects <- lapply(clusters_taken_list, function(cluster_sample_object) {
cluster_sample_object <- ScaleData(cluster_sample_object)
cluster_sample_object
})
三、画图
# f_ctrl
DimPlot(clusters_taken_list[[1]],cols = c("#0cd2ae","#52b0e6"), group.by = "cell_type_selection")
# f_tumor
DimPlot(clusters_taken_list[[2]], cols = c("#0cd2ae","#fcc000","#52b0e6"),group.by = "cell_type_selection")
# m_ctrl
DimPlot(clusters_taken_list[[3]], cols = c("#0cd2ae","#52b0e6"),group.by = "cell_type_selection")
# m_tumor
DimPlot(clusters_taken_list[[4]], cols = c("#0cd2ae","#fcc000","#52b0e6"),group.by = "cell_type_selection")
将四幅图简单合并一下并与文献原图比较
cell_type.png
往期单细胞数据挖掘实战：
