10分类变量热图绘制/SCI论文/科研/研究生/生信分析热点思路

分类变量热图绘制

今天小云绘制一下分类变量变量热图，绘制该图需要三个输入文件，代码如下：

1. 安装所需要的R包

BiocManager::install(“ComplexHeatmap”)

2. 导入需要的R包

library(ComplexHeatmap)

3. 导入数据

#差异表达分析中的logFC

logfc <- read.csv("logFc.csv", row.names = 1)

#差异分析中的P值

p.val <- read.csv("PVal.csv", row.names = 1)

#基因分组

genetype <- read.csv("genetype.csv")

3.代码展示

#为确保热图基因的顺序和分组文件的顺序是一致的

#因此按照基因分组文件里的顺序，对logfc和p.val两个文件排序

logfcOrdered <- logfc[genetype$gene,]

p.valOrdered <- p.val[genetype$gene,]

## 对logfc进行分类

logfcCat <- apply(logfcOrdered, 2, function(x){

    cut(x, breaks = c(-Inf, -2, -1, 1, 2, Inf),

        labels = c("< -2", "-2 - -1", "-1 - 1", "1 - 2", "> 2"))

})

rownames(logfcCat) <- rownames(logfcOrdered)

## 确保两个数据集的列名和行名顺序一致

if(!identical(rownames(logfcCat), rownames(p.valOrdered))) p.valOrdered <- p.valOrdered[rownames(logfcCat),]

if(!identical(colnames(logfcCat), colnames(p.valOrdered))) p.valOrdered <- p.valOrdered[colnames(logfcCat)]

## 把P> 0.05的数据标记为 P > 0.05

logfcCat[p.valOrdered >= 0.05] <- "P >= 0.05"

## 每个分类定义一个颜色

col_cat <- c("> 2" = "#A80C3A", "1 - 2" = "#ED5E57", "-1 - 1" = "#DDD3D2",

             "-2 - -1" = "#6B9AB7", "< -2" = "#2F5B89", "P >= 0.05" = "white")

cell_fun <- function(logfc, dataP, logfcCutoff = 1, PCutoff = 0.05,

                     darkcol = "black", lightcol = "white", digit = 2, fontsize  = 6){

    function(j, i, x, y, width, height, fill){

        if(abs(logfc[i,j]) > logfcCutoff & dataP[i,j] < PCutoff){

            grid.text(round(logfc, digit)[i, j], x, y,

                      gp = gpar(fontsize = fontsize, col  = lightcol))

        }else{

            grid.text(round(logfc, digit)[i, j], x, y,

                      gp = gpar(fontsize = fontsize, col  = darkcol))

        }

    }

}

an_col <- c("#ED1450", "#FCCA02", "#A7CE35", "#2C92DA", "#59574A", "#FFF8AD", "#A2238E", "#BCBDBF")

names(an_col) <- unique(genetype$type)

 

## 定义注释信息

row_an <-  HeatmapAnnotation(type = genetype$type, ##注释信息的内容

                             show_annotation_name = F, ## 是否显示注释的标题

                             col = list(type = an_col), ## 注释信息的颜色

                             show_legend = T,  ## 是否显示注释信息的说明

                             annotation_legend_param = list(title = "MMP Subtype"), ## 注释信息图例的标题

                             which = "row") #对行或者列进行注释

pdf("rankHeatmap.pdf", width = 8, height = 4)

Heatmap(matrix = logfcCat,

        name = "logFC", #主要图例的标题

        rect_gp = gpar(col = "NA", lwd = 1), #不画边框，或者用col = "grey"画灰色边框

        col = col_cat, #热图颜色

        row_names_side = "left",

        cell_fun = cell_fun(logfcOrdered, p.valOrdered),

        row_names_gp = gpar(fontsize = 8), #基因名字号

        column_names_gp = gpar(fontsize = 8), #肿瘤类型字号

        column_names_rot = 45, #肿瘤类型呈45度

        left_annotation = row_an) #左侧基因分类，如果不画，就筛掉这个参数

dev.off()

 

最终小云绘制出了分类变量热图，看起来效果不错，有需要的可以借鉴学习奥。