heatMatrix: Draw a heatmap of a given ScoreMatrix object
In BIMSBbioinfo/genomation: Summary, annotation and visualization of genomic data

Description Usage Arguments Value Examples

The function makes a heatmap out of given ScoreMatrix object. If desired it can use clustering using given clustering function (e.g. k-means) and plot cluster color codes as a sidebar. In addition, user can define groups of rows using 'group' argument.

heatMatrix(mat, grid = FALSE, col = NULL, xcoords = NULL, group = NULL,
  group.col = NULL, order = FALSE, user.order = FALSE, winsorize = c(0,
  100), clustfun = NULL, main = "", legend.name = NULL, cex.legend = 1,
  xlab = NULL, cex.main = 1, cex.lab = 1, cex.axis = 1,
  newpage = TRUE)

`mat`	a `ScoreMatrix` object
`grid`	if TRUE, grid graphics will be used. if FALSE, base graphics will be used on the top level, so users can use par(mfrow) or par(mfcol) prior to calling the function. Default:FALSE
`col`	a vector of colors, such as the ones created by heat.colors(10). If NULL (which is default), jet color scheme (common in matlab plots) will be used.
`xcoords`	a vector of numbers showing relative positions of the bases or windows. It must match the number of columns in the `ScoreMatrix`. Alternatively, it could be a numeric vector of two elements. Such as c(0,100) showing the relative start and end coordinates of the first and last column of the `ScoreMatrix` object.
`group`	a list of vectors of row numbers or a factor. This grouping is used for rowside colors of the heatmap. If it is a list, each element of the list must be a vector of row numbers. Names of the elements of the list will be used as names of groups. If `group` is a factor , it's length must match the number of rows of the matrix, and factor levels will be used as the names of the groups in the plot.
`group.col`	a vector of color names to be used at the rowside colors if `group` argument is given or `clustfun` function is given.
`order`	Logical indicating if the rows should be ordered or not (Default:FALSE). If `order=TRUE` the matrix will be ordered with rowSums(mat) values in descending order. If `group` argument is provided, first the groups will be ordered in descending order of sums of rows then, everything within the clusters will be ordered by sums of rows. If `clustfun` is given then rows within clusters will be order in descending order of sums of rows.
`user.order`	a numerical vector indicating the order of groups/clusters (it works only when `group` or `clustfun` argument is given).
`winsorize`	Numeric vector of two, defaults to c(0,100). This vector determines the upper and lower percentile values to limit the extreme values. For example, c(0,99) will limit the values to only 99th percentile, everything above the 99 percentile will be equalized to the value of 99th percentile.This is useful for visualization of matrices that have outliers.
`clustfun`	a function for clustering rows of `mat` that returns a vector of integers indicating the cluster to which each point is allocated (a vector of cluster membership), e.g. k-means algorithm with 3 centers: function(x) kmeans(x, centers=3)$cluster. By default FALSE.
`main`	a character string for the plot title
`legend.name`	a character label plotted next to the legend
`cex.legend`	A numerical value giving the amount by which legend axis marks should be magnified relative to the default
`xlab`	label a character string for x-axis of the heatmap
`cex.main`	A numerical value giving the amount by which plot title should be magnified
`cex.lab`	A numerical value giving the amount by which axis labels (including 'legend.name') should be magnified relative to the default.
`cex.axis`	A numerical value giving the amount by which axis marks should be magnified relative to the default
`newpage`	logical indicating if `grid.newpage()` function should be invoked if `grid=TRUE`.

returns clustering result invisibly, if clustfun is definied

data(cage)
data(promoters)
scores1=ScoreMatrix(target=cage,windows=promoters,strand.aware=TRUE,
                   weight.col="tpm")

set.seed(1000)

heatMatrix(mat=scores1,legend.name="tpm",winsorize=c(0,99),xlab="region around TSS",
           xcoords=-1000:1000,
           cex.legend=0.8,main="CAGE clusters on promoters",cex.lab=1,
           cex.axis=0.9,grid=FALSE)
           
## examples using clustering functions
## k-means
cl1 <- function(x) kmeans(x, centers=3)$cluster
set.seed(1000)
heatMatrix(mat=scores1,legend.name="tpm",winsorize=c(0,99),xlab="region around TSS",
         xcoords=-1000:1000,clustfun=cl1,
         cex.legend=0.8,main="CAGE clusters on promoters",cex.lab=1,
         cex.axis=0.9,grid=FALSE,
         user.order=c(1,3,2))

## hierarchical clustering
cl2 <- function(x) cutree(hclust(dist(x), method="complete"), k=3)
set.seed(1000)
heatMatrix(mat=scores1,legend.name="tpm",winsorize=c(0,99),xlab="region around TSS",
         xcoords=-1000:1000,clustfun=cl2,
         cex.legend=0.8,main="CAGE clusters on promoters",cex.lab=1,
         cex.axis=0.9,grid=FALSE)