Visualize fuzzy clustering results

Share:

Description

Takes in a time-course matrix and its clustering results as a cmeans clustering object. Produce a plot to visualize the clustering results.

Usage

1
2
fuzzPlot(Tc, clustObj, mfrow = c(1, 1), cols, min.mem = 0,
  new.window = FALSE, llwd = 3)

Arguments

Tc

a numeric matrix to be clustered. The columns correspond to the time-course and the rows correspond to phosphorylation sites.

clustObj

the clustering of Tc generated from cmeans or kmeans clustering.

mfrow

control the subplots in graphic window.

cols

color palette to be used for plotting. If the color argument remains empty, the default palette is used.

min.mem

phosphorylation sites with membership values below min.mem will not be displayed.

new.window

should a new window be opened for graphics.

llwd

line width. Default is 3.

Examples

1
2
3
4
5
6
7
# load the human ES phosphoprotoemics data (Rigbolt et al. Sci Signal. 4(164):rs3, 2011)
data(hES)
# apply cmeans clustering to partition the data into 11 clusters
library(e1071)
clustObj <- cmeans(hES, centers=11, iter.max=50, m=1.25)
# visualize clustering reuslts
fuzzPlot(hES, clustObj, mfrow = c(3,4))