knitr::opts_chunk$set(echo = TRUE)
The colorhcplot
package is a convenient tool for plotting colorful dendrograms where clusters, or sample groups, are highlighted by different colors. In order to generate a colorful dendrogram, colorhcplot()
function requires 2 mandatory arguments: hc
and fac
:
hc
is the result of a hclust()
call
fac
is a factor defining the grouping
The number of leaves of the dendrogram has to be identical to the length of fac (i.e., length(hc$labels) == length(fac) has to be TRUE). Also, the optional colors
argument (if supplied) has to have a length of 1 (single color) or equal to the length of the levels of fac
.
install.packages("colorhcplot") library(colorhcplot)
library(colorhcplot)
The first example is based on the USArrests dataset and compares the results of the standard plot
method applied to a hclust-class object and the output of colorhcplot()
. The use of simple arguments is illustrated.
data(USArrests) hc <- hclust(dist(USArrests), "ave") fac <- as.factor(c(rep("group 1", 10), rep("group 2", 10), rep("unknown", 30))) plot(hc) colorhcplot(hc, fac) colorhcplot(hc, fac, hang = -1, lab.cex = 0.8)
The second example is based on the UScitiesD dataset. Here we show how to specify custom colors for the colorhcplot()
call, using the colors
argument.
data(UScitiesD) hcity.D2 <- hclust(UScitiesD, "ward.D2") fac.D2 <-as.factor(c(rep("group1", 3), rep("group2", 7))) plot(hcity.D2, hang=-1) colorhcplot(hcity.D2, fac.D2, color = c("chartreuse2", "orange2")) colorhcplot(hcity.D2, fac.D2, color = "gray30", lab.cex = 1.2, lab.mar = 0.75)
The third example is based on a sample gene expression dataset, which is included in the colorhcplot
package. This illustrate how to use colorhcplot()
for exploration and analysis of genomic data.
data(geneData, package="colorhcplot") exprs <- geneData$exprs fac <- geneData$fac hc <- hclust(dist(t(exprs))) colorhcplot(hc, fac, main ="default", col = "gray10") colorhcplot(hc, fac, main="Control vs. Tumor Samples")
sessionInfo()
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