the ratio of observed to expected cell numbers (Ro/e)用于定量组织富集程度，可发现细胞群不同的组织偏好性。也可以看分组的细胞比例
有柱图 https://mp.weixin.qq.com/s/AfYRmLEw1oXE5S8zr5UQDA
KS代码：https://mp.weixin.qq.com/s/XxpguGv-DqhpezoOGlp3cA
我们改造后里面的参数可随意变化使用，可以使用不同的分组
Ro/e、Augur和miloR 的区别：https://mp.weixin.qq.com/s/MYIGKrskIWp5FXhSQffSYA
STARTRAC 做分组Ro/e ：https://mp.weixin.qq.com/s/EW4WWfizX98uM8cqS-S1gg
#------------------------------------------------------ 单细胞组织偏好性分析 OR
### 教程1 KS https://mp.weixin.qq.com/s/XxpguGv-DqhpezoOGlp3cA
#remotes::install_github("Japrin/sscVis")
library("sscVis")
library("data.table")
library("grid")
library("cowplot")
library("ggrepel")
library("readr")
library("plyr")
library("ggpubr")
library("ggplot2")
library("dplyr")
library("tidyr")
setwd("D:/Monocyte subsets/Covid-Recover/单细胞组织偏好性分析 Roe")
#加载函数
source("./test_function.R")
source("./draw_analysis.R")
#数据分析
A <- do.tissueDist(cellInfo.tb = immune.5@meta.data,#这里的输入文件需要的是整理好的有分组和细胞类型的metadata文件
out.prefix = "./ORplot", #设定导出的图文件名，自己设置
pdf.width = 5, #热图的宽设置
pdf.height = 6, #热图的高度设置
verbose=1, #设置为1文件以list形式存储
meta.cluster = 'celltype', #这里是细胞类型，也可以是seurat_clusters，名称没有要求，就是你细胞类型的列名
loc = 'group', #这里就是分组，metadata中分组的列名，至于命名没有要求
z.hi=4) #热图legend最大值，根据实际情况自己设置
#查看并保存文件
A$OR.dist.mtx #做热图数据，OR值
A$p.dist.tb #p值
A$OR.dist.tb #OR值
A$count.dist.melt.ext.tb#组合表，adjust-pvalue等
#自己做图
data <- A$count.dist.melt.ext.tb
write.csv(data, file = 'data.csv')
library(ggplot2)
library(RColorBrewer)
data <- read.csv("data.csv", header = T)
ggplot(data, aes(cid, rid)) +
geom_tile(aes(fill = OR), colour = "black", size = 0.6)+
scale_fill_gradientn(name='OR',
colours=colorRampPalette(rev(brewer.pal(n = 7, name ="RdYlBu")))(100))+
theme_minimal() +
theme(axis.title.x=element_blank(),
axis.ticks.x=element_blank(),
axis.title.y=element_blank(),
axis.text.y = element_text(size = 14,color = 'black'),
axis.text.x = element_text(size = 14,color = 'black')) +
scale_y_discrete(position = "right") +
geom_text(aes(label = sprintf("%.2f", OR)), vjust = 1, hjust = 1, size = 5)
#------------------------------------------------------ 单细胞组织偏好性分析 Ro/e -适合亚群比例分析
#Sys.getenv("GITHUB_PAT")
#Sys.unsetenv("GITHUB_PAT")
#devtools::install_github("Japrin/STARTRAC")
#install.packages("tictoc")
library(Startrac)
library(ggplot2)
library(tictoc)
library(ggpubr)
library(ComplexHeatmap)
library(RColorBrewer)
library(circlize)
library(tidyverse)
library(sscVis)
library(Seurat)
library(tidyverse)
library(readr)
library(qs)
library(BiocParallel)
library(ComplexHeatmap)
register(MulticoreParam(workers = 8, progressbar = TRUE))
dat <- immune.5@meta.data
### calTissueDist函数进行计算参考 https://mp.weixin.qq.com/s/EW4WWfizX98uM8cqS-S1gg
# 计算OR比值比时可改为method = "fisher"
R_oe <- calTissueDist(dat,
byPatient = F,
colname.cluster = "celltype", # 细胞类型
colname.patient = "orig.ident", # 不同样本
colname.tissue = "group", # 分组
method = "chisq",
min.rowSum = 0)
R_oe
#col_fun <- colorRamp2(c(min(R_oe, na.rm = TRUE), 1, max(R_oe, na.rm = TRUE)),
# c("blue", "white", "red"))
### 显示数值
Heatmap(as.matrix(R_oe),
show_heatmap_legend = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
row_names_side = 'right',
show_column_names = TRUE,
show_row_names = TRUE,
col = col_fun,
row_names_gp = gpar(fontsize = 10),
column_names_gp = gpar(fontsize = 10),
heatmap_legend_param = list(
title = "Ro/e Index", # 自定义图注名称
at = seq(0.5, 2, by = 0.5), # 例刻度的位置/自己的数据必须修改一下！
labels = seq(0.5, 2, by = 0.5) # 每个刻度的标签/自己的数据必须修改一下！
),
cell_fun = function(j, i, x, y, width, height, fill) {
grid.text(sprintf("%.2f", R_oe[i, j]), x, y, gp = gpar(fontsize = 8, col = "black"))
}
)
### 可视化参考 https://mp.weixin.qq.com/s/MYIGKrskIWp5FXhSQffSYA
library(ComplexHeatmap)
library(grid)
# 定义颜色映射函数（根据你的数据范围调整颜色映射）
col_fun <- colorRamp2(c(0, 0.5, 1.5, 2), c("blue", "green", "orange", "red"))
### 显示 + - 号
# 绘制热图
Heatmap(as.matrix(R_oe),
show_heatmap_legend = TRUE,
cluster_rows = TRUE,
cluster_columns = TRUE,
row_names_side = 'right',
show_column_names = TRUE,
show_row_names = TRUE,
col = col_fun,
row_names_gp = gpar(fontsize = 18), # 坐标字体
column_names_gp = gpar(fontsize = 18), # 坐标字体
column_names_rot = 45, # 将列名倾斜 45°
heatmap_legend_param = list(
title = "Ro/e Index",
at = seq(0.5, 2, by = 0.5),
labels = seq(0.5, 2, by = 0.5)
),
cell_fun = function(j, i, x, y, width, height, fill) {
value <- R_oe[i, j]
if (value > 1) {
label <- "+++"
} else if (value > 0.8) {
label <- "++"
} else if (value >= 0.2) {
label <- "+"
} else if (value > 0) {
label <- "+/-"
} else if (value == 0) {
label <- "-"
} else {
label <- NA # 如果有负值或其他异常值
}
grid.text(label, x, y, gp = gpar(fontsize = 30 , col = "black")) # 调整里面
}
)