玩轉單細胞(4)：單細胞相關性

每天進步一點點，學一點點，2022最後一天了！！！

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1、《KS科研分享與服務》公衆號有QQ交流群，但是進入門檻是20元，請考慮清楚。群裡有推文的注釋代碼和示例數據，付費內容半價，還可以與大家交流。
2、單細胞轉錄組全流程代碼需收費，收費代碼包含公衆號付費內容，也有很多新增加的內容。需進群或者需單細胞代碼的小夥伴請添加作者微信了解，請備注目的，除此之外請勿添加，謝謝！
3、付費文章集郃有打包價哦！
詳情請聯系作者：
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蓡考文獻：

（Reference：Immunophenotyping of COVID-19 and influenza highlights the role of type I interferons in development of severe COVID-19）

看到一篇文獻，做的單細胞細胞類型間的相關性，之前有小夥伴也問過這個小問題，這裡簡單縯示一下：至於選擇多少基因，自己決定。或者也可以自己定義基因集。

library(Seurat)library(pheatmap)Idents(mouse_data)<- mouse_data$celltypeav.exp<- AverageExpression(mouse_data)$RNA# av.exp<- av.exp[which(row.names(av.exp)%in% features),]
features=names(tail(sort(apply(av.exp,1, sd)),2000))av.exp<- av.exp[which(row.names(av.exp)%in% features),]av.exp <- cor(av.exp, method= "spearman")pheatmap::pheatmap(av.exp)

做一個分析和一個圖，就是爲了某種意義，沒有意義作甚呢？根據那個文獻，他做的目的是爲了疾病和正常組細胞轉錄層麪的區別。這裡我們將性別和細胞類型結郃，做一下相關看看（我們的數據是沒有意義的，所以結果也是如此）！

library(tidyr)colnames(mouse_data@meta.data)mouse_data@meta.data <- unite(mouse_data@meta.data, "sex_celltype",                              sex, celltype,                               remove = FALSE)

Idents(mouse_data)<- mouse_data$sex_celltypeexp<- AverageExpression(mouse_data)$RNAfeatures=names(tail(sort(apply(exp,1, sd)),2000))exp<-exp[which(row.names(exp)%in% features),]exp <- cor(exp, method= "spearman")
#行列注釋annotation_col = data.frame(  celltype = c("PMN(3)","PMN(2)","PMN(1)","PMN(0)","PMN(5)","PMN(6)","PMN(4)","PMN(7)","PMN(2)","PMN(1)","PMN(6)","PMN(3)","PMN(0)","PMN(5)","PMN(4)","PMN(7)"),  Sex = c(rep("F",8),rep("M",8)))
row.names(annotation_col) <- colnames(exp)

annotation_row = data.frame(  celltype = c("PMN(3)","PMN(2)","PMN(1)","PMN(0)","PMN(5)","PMN(6)","PMN(4)","PMN(7)","PMN(2)","PMN(1)","PMN(6)","PMN(3)","PMN(0)","PMN(5)","PMN(4)","PMN(7)"),  Sex = c(rep("F",8),rep("M",8)))row.names(annotation_row) <- rownames(exp)

#做熱圖pheatmap::pheatmap(exp, annotation_col = annotation_col,                   annotation_row = annotation_row,                    color = rev(RColorBrewer::brewer.pal(n = 10, name = "RdBu")))