The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
# Introduction # Heat map and clustering are used frequently in expression analysis studies for data visualization and quality control. Simple clustering and heat map can be produced from the “heatmap” function package in R. However, the “heatmap” function lacks functionality and customizability, preventing it from generating advanced heat maps and dendrograms. To tackle the limitations of “heatmap” function, we have developed an R package “heatmap3” which significantly improves the original “heatmap” function by adding several more powerful and convenient features. “heatmap3” packages allows user to produce highly customizable state of art heatmap and dendrogram. The heatmap3 package is developed based on the “heatmap” function in R language and it is completely compatible with it. And the new features of “heatmap3” include highly customizable legend and side annotation, a wider range of color selections, new labeling features which allow user to define multiple layers of phenotype variables and automatically conduct association test based on the phenotypes provided. Additional features such as different agglomeration methods for estimating distance between two samples are also added for clustering.
# Download and install # You can install heatmap3 package in R from CRAN by following codes:
install.packages("heatmap3")# Example # After you have installed heatmap3 package. You can enter R and use following R codes to see the examples for it.
#Load heatmap3 package
library(heatmap3)
#View help files
?heatmap3
#The examples of heatmap3 and other functions
example(heatmap3)
example(showLegend)
example(showAnn)
example(colByValue)We used the TCGA BRCA data to generate two example figures, which were used in our paper.
First you will need to download the count data. We generate the count data based on the TCGA BRCA samples. You can use the following codes in Linux system to download the count data. Or you can download it in github release page.
wget https://github.com/slzhao/heatmap3/releases/download/example/allSample_edgeR_result.csv
wget https://github.com/slzhao/heatmap3/releases/download/example/BRCA_30Samples.csv
wget https://github.com/slzhao/heatmap3/releases/download/example/BRCA_30Samples_clinic.csvHere are the R codes to generate the figures.
#Assume you have already installed heatmap3 package. And the 3 example file were download in current working directory. You can use the following codes in R to generate the figures.
#Prepare expression data
counts<-read.csv("BRCA_30Samples.csv",header=T,row.names=1)
#Prepare column side annotation
clinic<-read.csv("BRCA_30Samples_clinic.csv",header=T,row.names=1)
#Prepare row side color bar annotation
edgeR_result<-read.csv("allSample_edgeR_result.csv",header=T,row.names=1)
temp1<-(edgeR_result$logFC)
temp2<--log10(edgeR_result$FDR)
temp1<-colByValue(as.matrix(temp1),range=c(-4,4),col=colorRampPalette(c('chartreuse4','white','firebrick'))(1024))
temp2<-colByValue(as.matrix(temp2),range=c(0,5),col=heat.colors(1024))
colGene<-cbind(temp1,temp2)
row.names(colGene)<-row.names(edgeR_result)
colnames(colGene)<-c("log2FC","-Log10P")
#Generate Figure1
#counts, colGene and clinic were read throught the csv file
##Assume counts has counts information for each gene, colGene has the colors for each gene, clinic has the clinic information for each sample
temp<-apply(counts,1,sd)
selectedGenes<-rev(order(temp))[1:500]
heatmap3(counts[selectedGenes,],labRow="",margin=c(7,0),RowSideColors=colGene[selectedGenes,],ColSideCut=0.85,ColSideAnn=clinic,ColSideFun=function(x) showAnn(x),ColSideWidth=1.2,balanceColor=T)
#Generate Figure2
heatmap3(counts,topN=c(500,3000,nrow(counts)),Rowv=NA,labRow="",margin=c(7,0),RowSideColors=colGene,ColSideCut=0.85,ColSideAnn=clinic,ColSideFun=function(x) showAnn(x),ColSideWidth=1.2,balanceColor=T)Here is the environment (including the version of packages).
R version 3.0.2 (2013-09-25)
Platform: x86_64-w64-mingw32/x64 (64-bit)
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] heatmap3_1.0.3
loaded via a namespace (and not attached):
[1] fastcluster_1.1.13 tools_3.0.2 These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.