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
        }
)