clusterize: Hierarchical clustering heat maps

In LPS: Linear Predictor Score, for Binary Inference from Multiple Continuous Variables

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.

Author(s)

Sylvain Mareschal

See Also

heat.map, heatmap, hclust, dist

Examples

  # 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)

Example output

The "ward" method has been renamed to "ward.D"; note new "ward.D2"
The "ward" method has been renamed to "ward.D"; note new "ward.D2"
The "ward" method has been renamed to "ward.D"; note new "ward.D2"
The "ward" method has been renamed to "ward.D"; note new "ward.D2"

LPS documentation built on May 29, 2021, 5:07 p.m.