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Last data update: 2014.03.03

R: Draw hierarchical tree of gene expression data with a...

prettyDend

R Documentation

Draw hierarchical tree of gene expression data with a colorbar for numerous class vectors

Description

Function which performs a hierarchical cluster analysis of data, drawing a dendrogram, with colorbars for different sample covariate beneath the dendrogram

Usage

prettyDend(dataset, labels = NULL, title = "", classvec = NULL,covars=1,returnTree=FALSE, getPalette=getcol,...)

Arguments

`dataset`	a `matrix`, `data.frame`, `ExpressionSet` or `marrayRaw-class`. If the input is gene expression data in a `matrix` or `data.frame`. The rows and columns are expected to contain the variables (genes) and cases (array samples) respectively.
`labels`	Vector, labels to be placed on samples in plots. Default is rownames(dataset).
`title`	Character, label to be placed on plots. Default is `NULL`.
`classvec`	A `factor` or `vector` or `matrix` or `data.frame` which describes the classes in columns of the `dataset`. Default is `NULL`.
`covars`	Numeric. The columns of the data.frame classve to be used as class vectors. These will be displayed as color bars under the dendrogram. The default is 1 (column 1).
`returnTree`	Logical. Return the hieracrhical cluster analysis results. Default is `FALSE`.
`getPalette`	Function, which generates a palette of colors. The default uses `getcol` function in made4. Other examples are provided below
`...`	further arguments passed to or from other methods.

Details

The hierarchical plot is produced using average linkage cluster analysis with 1- Pearson's correlation metric. The default set of colors used to generate the color bars of the plots can be changed (see example below). By default, if there is only two levels in the factor, the colors will be black and grey.

Author(s)

Aedin Culhane

Examples

  data(khan)
  logkhan<-log2(khan$train)

 
 # Get a character vector which defines which khan samples are cell lines or tissue sample
  khanAnnot= cbind(as.character(khan$train.classes),khan$cellType)
  print(khanAnnot[1:3,])

# Add 2 color bar, one for cancer subtype, another for cell type under dendrogram
  prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes)

# To change the palette of colors
# Use topo.colors(), see colors() for more help on inbuilt palettes

  prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes, getPalette=topo.colors)

# To use RColorBrewer Palettes
  library(RColorBrewer)

  # Use RColorBrewer Dark2 which contains 8 colors 
  prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes, getPalette=function(x) brewer.pal(8,"Dark2")[1:x])

  # Use RColorBrewer Set1 which contains 9 colors 
  prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes, getPalette=function(x) brewer.pal(9,"Set1")[1:x])

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(made4)
Loading required package: ade4
Loading required package: RColorBrewer
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: scatterplot3d
> png(filename="/home/ddbj/snapshot/RGM3/R_BC/result/made4/prettyDend.Rd_%03d_medium.png", width=480, height=480)
> ### Name: prettyDend
> ### Title: Draw hierarchical tree of gene expression data with a colorbar
> ###   for numerous class vectors
> ### Aliases: prettyDend
> ### Keywords: hplot manip
> 
> ### ** Examples
> 
>   data(khan)
>   logkhan<-log2(khan$train)
> 
>  
>  # Get a character vector which defines which khan samples are cell lines or tissue sample
>   khanAnnot= cbind(as.character(khan$train.classes),khan$cellType)
>   print(khanAnnot[1:3,])
     [,1]  [,2]
[1,] "EWS" "T" 
[2,] "EWS" "T" 
[3,] "EWS" "T" 
> 
> # Add 2 color bar, one for cancer subtype, another for cell type under dendrogram
>   prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes)
     groups   cols   
[1,] "RMS"    "red"  
[2,] "NB"     "blue" 
[3,] "EWS"    "green"
[4,] "BL-NHL" "brown"
     groups cols    
[1,] "T"    "black" 
[2,] "C"    "grey60"
> 
> # To change the palette of colors
> # Use topo.colors(), see colors() for more help on inbuilt palettes
> 
>   prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes, getPalette=topo.colors)
     groups   cols       
[1,] "RMS"    "#4C00FFFF"
[2,] "NB"     "#00E5FFFF"
[3,] "EWS"    "#00FF4DFF"
[4,] "BL-NHL" "#FFFF00FF"
     groups cols    
[1,] "T"    "black" 
[2,] "C"    "grey60"
> 
> # To use RColorBrewer Palettes
>   library(RColorBrewer)
> 
>   # Use RColorBrewer Dark2 which contains 8 colors 
>   prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes, getPalette=function(x) brewer.pal(8,"Dark2")[1:x])
     groups   cols     
[1,] "RMS"    "#1B9E77"
[2,] "NB"     "#D95F02"
[3,] "EWS"    "#7570B3"
[4,] "BL-NHL" "#E7298A"
     groups cols    
[1,] "T"    "black" 
[2,] "C"    "grey60"
> 
>   # Use RColorBrewer Set1 which contains 9 colors 
>   prettyDend(logkhan, classvec=khanAnnot, covars = c(1,2), labels=khan$train.classes, getPalette=function(x) brewer.pal(9,"Set1")[1:x])
     groups   cols     
[1,] "RMS"    "#E41A1C"
[2,] "NB"     "#377EB8"
[3,] "EWS"    "#4DAF4A"
[4,] "BL-NHL" "#984EA3"
     groups cols    
[1,] "T"    "black" 
[2,] "C"    "grey60"
> 
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>