绘制热图

首先准备数据：

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gene_id	id	TCGA-2J-AAB101A-11R-A41B-07	TCGA-2J-AAB4-01A-12R-A41B-07	TCGA-2J-AAB6-01A-11R-A41B-07	TCGA-2J-AAB8-01A-12R-A41B-07	TCGA-2J-AAB9-01A-11R-A41B-07	TCGA-2J-AABA-01A-21R-A41B-07
APLP2	334	20091.2	23546.3	18321.6	14173.4	18687	18471.9
OLR1	4973	397.561	925.084	766.082	585.664	755.627	177.861
PLXDC1	57125	797.507	600.088	628.012	744.536	849.946	421.425
SEMA3C	10512	965.366	797.98	5651.46	1521.03	2662.38	3890.4

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用R绘制热图：首先要安装pheatmap包

install.packages("pheatmap")

 然后根据表达量绘制，横轴作为样本（分为两组），纵轴作为基因。

1.导入数据

data <- read.table("gene.txt",header = T)

data_2 <- data[-1,-c(1,2)] ＃只保留表达值
data_2<-apply(data_2,2, as.numeric) ＃将其转换为数值型，有时候你的数据可能是经过不同格式转换过来的，需要注意
as.numeric(data_2)
data_3 <- matrix(as.numeric(data_2),nrow = nrow(data_2)) ＃制作矩阵
rownames(data_3)<-data[-1,]$gene_id  ＃添加行名基因
colnames(data_3)<-colnames(data_2) ＃添加列名样本
data_3[data_3 <= 0] = 0.0001
data_3[1:4,1:4]

2. log数据

logData = apply(data_3, 2, log2)

3. 用preprocessCore包做标准化，此步骤在你不需要的时候可以省略

library("preprocessCore")
logData = normalize.quantiles(logData)
rownames(logData) = rownames(data_3)
colnames(logData) = colnames(data_3)
boxplot(logData)

 4. 热图，用heatmap

group<-factor(c(rep('group1',22),rep('group2',22))) #分组
patientcolors = c("green","red")[group] #将两组分别标记为红色和绿色
##logData[logData<= "-lnf"] = -3 
heatmap(logData, col = colorRampPalette ( rev ( c ( "red" , "white" , "green" ) ) ) ( 102 ),Colv=NA, ColSideColors=patientcolors, cexRow=0.5)
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5. 热图，用pheatmap

library(pheatmap)
annotation = data.frame(Class=group)
rownames(annotation) = colnames(logData) 
pheatmap( logData, annotation=annotation, color = colorRampPalette(c("darkgreen", "gray99", "red"))(500), cellwidth = 10, cellheight = 3,scale="row", cluster_rows = TRUE,
          cluster_cols = FALSE, show_rownames=T, show_colnames=T, fontsize=8, fontsize_col=6,fontsize_row=3, border_color= NA,filename = "test.pdf") 
#
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如果有人要测试，可以用我的数据：

链接: https://pan.baidu.com/s/1bpkoDnp 密码: waxm

具体的修改，大家可以参见其参数设置。

https://cran.r-project.org/web/packages/pheatmap/pheatmap.pdf