LPS (version 1.0.16)

clusterize: Hierarchical clustering heat maps

Description

This function draws a heat map ordered according to hierarchical clusterings, similarly to heatmap. It offers more control on layout and allows multiple row annotations.

hclust.ward is derivated from 'stats' package hclust, with an alternative default (as arguments can not be passed to it).

dist.COR mimics 'stats' package dist, computing distances as 1 - Pearson's correlation coefficient.

Usage

clusterize(expr, side = NULL, cex.col = NA, cex.row = NA, mai.left = NA,
    mai.bottom = NA, mai.right = 0.1, mai.top = 0.1, side.height = 1, side.col = NULL,
    side.srt = 0, side.cex = 1, col.heatmap = heat(), zlim = "0 centered",
	zlim.trim = 0.02, norm = c("rows", "columns", "none"), norm.clust = TRUE,
	norm.robust = FALSE, customLayout = FALSE, getLayout = FALSE, plot = TRUE,
	widths = c(1, 4), heights = c(1, 4), order.genes = NULL, order.samples = NULL,
	fun.dist = dist.COR, fun.hclust = hclust.ward, clust.genes = NULL,
	clust.samples = NULL)
  dist.COR(input)
  hclust.ward(input)

Arguments

expr

A numeric matrix, holding features (genes) in columns and observations (samples) in rows. Rows and columns will be ordered according to hierarchical clustering results.

side

To be passed to heat.map.

cex.col

To be passed to heat.map.

cex.row

To be passed to heat.map.

mai.left

To be passed to heat.map.

mai.bottom

To be passed to heat.map.

mai.right

To be passed to heat.map.

mai.top

To be passed to heat.map.

side.height

To be passed to heat.map.

side.col

To be passed to heat.map.

side.srt

To be passed to heat.map.

side.cex

To be passed to heat.map.

col.heatmap

To be passed to heat.map.

zlim

To be passed to heat.map.

zlim.trim

To be passed to heat.map.

norm

To be passed to heat.map.

norm.clust

Single logical value, whether to apply normalization before clustering or after. Normalization applied depends on norm.

norm.robust

To be passed to heat.map.

customLayout

Single logical value, as layout does not allow nested calls, set this to TRUE to make your own call to layout and embed this plot in a wider one.

getLayout

Single logical value, whether to only return the layout arguments that would be used with the set of arguments provided or not. It can prove useful to build custom layouts, e.g. merging this plot to an other. See also customLayout.

plot

To be passed to heat.map.

widths

To be passed to layout.

heights

To be passed to layout.

order.genes

A function taking the gene dendrogram and expr as arguments, and returning the same dendrogram ordered in a custom way.

order.samples

A function taking the sample dendrogram and expr as arguments, and returning the same dendrogram ordered in a custom way.

fun.dist

A function to be used for distance computation in clustering. Default value uses 1 - Pearson's correlation as distance. See dist for further details.

fun.hclust

A function to be used for agglomeration in clustering. See hclust for further details.

clust.genes

If not NULL, an object coercible to the dendrogram class (typically the output from hclust()) to use instead of a fresh hierarchical clustering of genes. The FALSE value can also be used to disable computation and/or plotting of the dendrogram.

clust.samples

If not NULL, an object coercible to the dendrogram class (typically the output from hclust()) to use instead of a fresh hierarchical clustering of samples. The FALSE value can also be used to disable computation and/or plotting of the dendrogram.

input

See hclust and dist respectively for further details.

Value

clusterize invisibly returns the same list as heat.map, plus :

genes

The gene dendrogram.

samples

The sample dendrogram.

See hclust and dist respectively for the other functions.

See Also

heat.map, heatmap, hclust, dist

Examples

# NOT RUN {
  # Data with features in columns
  data(rosenwald)
  group <- rosenwald.cli$group
  expr <- t(rosenwald.expr)[,1:100]
  
  # NA imputation (feature's mean to minimize impact)
  f <- function(x) { x[ is.na(x) ] <- round(mean(x, na.rm=TRUE), 3); x }
  expr <- apply(expr, 2, f)
  
  # Simple heat map
  clusterize(expr)
  
  # With annotation (row named data.frame)
  side <- data.frame(group, row.names=rownames(expr))
  clusterize(expr, side=side)
# }