supraHex (version 1.10.0)

visHeatmap: Function to visualise input data matrix using heatmap

Description

visHeatmap is supposed to visualise input data matrix using heatmap. Note: this heatmap displays matrix in a bottom-to-top direction

Usage

visHeatmap(data, scale = c("none", "row", "column"), row.metric =
c("none",
"pearson", "spearman", "kendall", "euclidean", "manhattan", "cos",
"mi"),
row.method = c("ward", "single", "complete", "average", "mcquitty",
"median", "centroid"), column.metric = c("none", "pearson", "spearman",
"kendall", "euclidean", "manhattan", "cos", "mi"), column.method =
c("ward",
"single", "complete", "average", "mcquitty", "median", "centroid"),
colormap = c("bwr", "jet", "gbr", "wyr", "br", "yr", "rainbow", "wb"),
ncolors = 64, zlim = NULL, row.cutree = NULL,
row.colormap = c("rainbow"), column.cutree = NULL,
column.colormap = c("rainbow"), ...)

Arguments

data

an input gene-sample data matrix used for heatmap

scale

a character indicating when the input matrix should be centered and scaled. It can be one of "none" (no scaling), "row" (being scaled in the row direction), "column" (being scaled in the column direction)

row.metric

distance metric used to calculate the distance metric between rows. It can be one of "none" (i.e. no dendrogram between rows), "pearson", "spearman", "kendall", "euclidean", "manhattan", "cos" and "mi". See details at http://suprahex.r-forge.r-project.org/sDistance.html

row.method

the agglomeration method used to cluster rows. This should be one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". See 'Note' below for details

column.metric

distance metric used to calculate the distance metric between columns. It can be one of "none" (i.e. no dendrogram between rows), "pearson", "spearman", "kendall", "euclidean", "manhattan", "cos" and "mi". See details at http://suprahex.r-forge.r-project.org/sDistance.html

column.method

the agglomeration method used to cluster columns. This should be one of "ward", "single", "complete", "average", "mcquitty", "median" or "centroid". See 'Note' below for details

colormap

short name for the colormap. It can be one of "jet" (jet colormap), "bwr" (blue-white-red colormap), "gbr" (green-black-red colormap), "wyr" (white-yellow-red colormap), "br" (black-red colormap), "yr" (yellow-red colormap), "wb" (white-black colormap), and "rainbow" (rainbow colormap, that is, red-yellow-green-cyan-blue-magenta). Alternatively, any hyphen-separated HTML color names, e.g. "blue-black-yellow", "royalblue-white-sandybrown", "darkgreen-white-darkviolet". A list of standard color names can be found in http://html-color-codes.info/color-names

ncolors

the number of colors specified over the colormap

zlim

the minimum and maximum z/patttern values for which colors should be plotted, defaulting to the range of the finite values of z. Each of the given colors will be used to color an equispaced interval of this range. The midpoints of the intervals cover the range, so that values just outside the range will be plotted

row.cutree

an integer scalar specifying the desired number of groups being cut from the row dendrogram. Note, this optional is only enabled when the row dengrogram is built

row.colormap

short name for the colormap to color-code the row groups (i.e. sidebar colors used to annotate the rows)

column.cutree

an integer scalar specifying the desired number of groups being cut from the column dendrogram. Note, this optional is only enabled when the column dengrogram is built

column.colormap

short name for the colormap to color-code the column groups (i.e. sidebar colors used to annotate the columns)

...

additional graphic parameters. Type ?heatmap for the complete list.

Value

invisible

See Also

visHeatmap

Examples

# 1) generate data with an iid matrix of 100 x 9
data <- cbind(matrix(rnorm(100*3,mean=0,sd=1), nrow=100, ncol=3),
matrix(rnorm(100*3,mean=0.5,sd=1), nrow=100, ncol=3),
matrix(rnorm(100*3,mean=-0.5,sd=1), nrow=100, ncol=3))
colnames(data) <- c("S1","S1","S1","S2","S2","S2","S3","S3","S3")

# 2) prepare colors for the column sidebar
lvs <- unique(colnames(data))
lvs_color <- visColormap(colormap="rainbow")(length(lvs))
my_ColSideColors <- sapply(colnames(data), function(x)
lvs_color[x==lvs])

# 3) heatmap with row dendrogram (with 10 color-coded groups)
visHeatmap(data, row.metric="euclidean", row.method="average",
colormap="gbr", zlim=c(-2,2),
ColSideColors=my_ColSideColors, row.cutree=10, row.colormap="jet",
labRow=NA)