plotDiffCorrGroup: Plot DiffCorr group

In DiffCorr: Analyzing and Visualizing Differential Correlation Networks in Biological Data

Plot DiffCorr group

Description

Plot DiffCorr group

Usage

plotDiffCorrGroup(
  data,
  groups1 = NULL,
  groups2 = NULL,
  group1.no = NULL,
  group2.no = NULL,
  g1,
  g2,
  g1.order = NULL,
  g2.order = NULL,
  title1 = NULL,
  title2 = NULL,
  ...
)

Arguments

data	a data matrix or data frame
groups1	a vector of row group membership as produced by cutree under condition 1
groups2	a vector of row group membership as produced by cutree under condition 2
group1.no	the group number to be plotted (condition 1)
group2.no	the group number to be plotted (condition 2)
g1	a vector describing the columns of the data belonging to condition 1
g2	a vector describing the columns of the data belonging to condition 2
g1.order	whether or not to order the columns of the data matrix for condition 1. If "average", then the columns are ordered by the average expression value. If the name of a gene (row), then the columns are orderd according to the expression levels of that gene. If NULL, columns remain in their original order.
g2.order	See g1.order
title1	A title for the left hand graph
title2	A title for the right hand graph
...	other parameters to be passed to this function

Value

a graph

Author(s)

Atsushi Fukushima

Examples

library(pcaMethods)
data(golub, package = "multtest")
hc.mol1 <- cluster.molecule(golub[, 1:27], "pearson", "average")
hc.mol2 <- cluster.molecule(golub[, 28:38], "pearson", "average")
g1 <- cutree(hc.mol1, h=0.4)
g2 <- cutree(hc.mol2, h=0.4)
##
plotDiffCorrGroup(golub, g1, g2, 21, 24, 1:27, 28:38,
                   scale.center=TRUE, scale.scale=TRUE,
                   ylim=c(-5,5))

DiffCorr documentation built on Aug. 25, 2023, 5:16 p.m.