Getting started with colorhcplot

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Damiano Fantini

February 19, 2018

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:

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

install.packages("colorhcplot")
library(colorhcplot)

Example 1: using the USArrests dataset

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)

Example 2: use the “ward.D2” algorithm and the UScitiesD dataset

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)

Example 3: use gene expression data

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

sessionInfo()

## R version 3.4.3 (2017-11-30)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.3 LTS
## 
## Matrix products: default
## BLAS: /usr/lib/libblas/libblas.so.3.6.0
## LAPACK: /usr/lib/lapack/liblapack.so.3.6.0
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] colorhcplot_1.3.1
## 
## loaded via a namespace (and not attached):
##  [1] compiler_3.4.3  backports_1.1.2 magrittr_1.5    rprojroot_1.3-2
##  [5] htmltools_0.3.6 tools_3.4.3     yaml_2.1.16     Rcpp_0.12.15   
##  [9] stringi_1.1.6   rmarkdown_1.8   knitr_1.19      stringr_1.2.0  
## [13] digest_0.6.14   evaluate_0.10.1

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.