colorhcplot: Colorful Hierarchical Clustering Dendrograms

In colorhcplot: Colorful Hierarchical Clustering Dendrograms

Colorful Hierarchical Clustering Dendrograms

Description

Build colorful dendrograms based on a "hclust-class" object and a factor describing the sample groups. Leaves belonging to different groups are identified by colors, and the resulting plot enables detection of pure clusters where all leaves belong to the same group.

Usage

colorhcplot(
  hc,
  fac,
  hang = 0.1,
  main = "Cluster Dendrogram",
  colors = NULL,
  lab.cex = 1,
  ylim = NULL,
  lwd = 3,
  las = 1,
  lab.mar = 0.55
)

Arguments

hc	hclust-class object, typically the result of a 'hclust()' function call.
fac	factor that defines the grouping.
hang	hang value, as in hclust. This is the fraction of the plot height by which labels should hang below the rest of the plot. A negative value will align all labels at the bottom of the plot.
main	string, title of the dendrogram plot.
colors	NULL or a character vector of length 1 or having the same length as the number of levels in fac. This argument defines the palette for the plot.
lab.cex	numeric value for adjusting the font size of the leaf labels (and legend text).
ylim	numeric, defines the minimum and maximum value of the y-axis of the plot.
lwd	numeric value that defines the width (in points) of the lines of the dendogram.
las	numeric value, graphic parameter for the orientation of the y-axis tick labels.
lab.mar	numeric value, fraction of the plot area that is reserved for the labels (at the bottom of the plot).

Details

In order to generate a colorful dendrogram, the colorhcplot() function requires 2 mandatory arguments: hc and fac. hc is the result of a hclust() call, while fac is a factor defining the groups. The number of leaves of the dendrogram has to be identical to the length of fac.

Value

Calling colorhcplot() returns a colorful dendrogram plot.

Note

Online colorhcplot() function reference at: http://www.biotechworld.it/bioinf/2015/09/30/colorful-hierarchical-clustering-dendrograms-with-r/

Author(s)

Damiano Fantini <[e-mail]>

See Also

hclust

Examples

 
### Example 1, using the USArrests dataset
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)

### Example 2: use the "ward.D2" algorithm and
### the UScitiesD dataset
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)
 
### Example 3: use gene expression 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") 
 
 
 

colorhcplot documentation built on June 8, 2025, 11:07 a.m.