ComplexHeatmap

source("https://bioconductor.org/biocLite.R")
biocLite("ComplexHeatmap")
biocLite("colorspace")
library(ComplexHeatmap)

# read the drug response data
data1<-read.table("AUCs_with_names_of_Drugs.txt", header=T, sep="\t", na.strings = "NA", fill=T)
row.names(data1)<-data1$SAMPLE
r.data1<-data1[,2:the dimensions]
r.data1_matrix<-data.matrix(r.data1)

# set the order of raw and column if needed
data2<-read.table("crc_final_colorder.txt", header=T, sep="\t", na.strings = "NA", fill=T)
v.data2<-as.vector(t(data2))

data3<-read.table("crc_final_roworder.txt", header=T, sep="\t", na.strings = "NA", fill=T)
v.data3<-as.vector(t(data3))

# draw heatmap defaulting clustering 
Heatmap(r.data1_matrix, name = "AUC")

# draw heatmap k-means clustering 
Heatmap(r.data1_matrix, name = "AUC", row_km = 2, row_km_repeats = 100, column_km = 3, column_km_repeats = 100)

# make the plot
dev.copy(tiff,"CRCHETERODRUGALL_HEAT_Kmeans.tiff",width=22.7, height=11, units="in",res=200) 
dev.off()