swamp (version 1.5.1)

dense.plot: Density plots of feature associations in observed and permuted data

Description

The function plots the distribution of feature associations for a specified sample annotation for both observed and reshuffled data.

Usage

dense.plot(feature.assoc, lty = 1:2, col = 1:2, lwd = c(2, 2), ylab = "", 
           main = "", cex.main = 1, cex.lab = 1, cex.axis = 1)

Arguments

feature.assoc

A list of feature associations, typically created by the function feature.assoc(). (If not created by feature.assoc() the list has to contain the elements observed, permuted and method.)

lty

a numeric vector containing the line types for the observed and permuted density lines. default=1:2.

col

the colors for the observed and permuted density lines. default=1:2.

lwd

the line widths. default=c(2,2).

ylab

optional labeling of y-axis.

main

optional titel.

cex.main

optional titel font size.

cex.lab

optional axis label font size.

cex.axis

optional axis font size.

Details

The function plots the distribution of associations of features with a sample annotation calculated by feature.assoc(). The function uses plot.density() for the observed data and adds the permuted data using lines(density()). The x-axis is dependent on the method used to measure association, e.g. if the method was "correlation", then xlim is c(-1,1) and xlab="Corrlation".

Examples

# NOT RUN {
## data as a matrix
set.seed(100)
g<-matrix(nrow=1000,ncol=50,rnorm(1000*50),dimnames=list(paste("Feature",1:1000),
   paste("Sample",1:50)))
g[1:100,26:50]<-g[1:100,26:50]+1 # the first 100 features show
# higher values in the samples 26:50
## patient annotations as a data.frame, annotations should be numbers and factor
# but not characters.
## rownames have to be the same as colnames of the data matrix 
set.seed(200)
o<-data.frame(Factor1=factor(c(rep("A",25),rep("B",25))),
              Factor2=factor(rep(c("A","B"),25)),
              Numeric1=rnorm(50),row.names=colnames(g))

# calculate the associations to Factor 1
res4a<-feature.assoc(g,o$Factor1,method="correlation")
res4b<-feature.assoc(g,o$Factor1,method="t.test",g1=res4a$permuted.data) 
   # uses t.test instead, reuses the permuted data generated in res4a
res4c<-feature.assoc(g,o$Factor1,method="AUC",g1=res4a$permuted.data) 
   # uses AUC instead, reuses the permuted data generated in res4a

# plot distribution of associations in observed and permuted data
dense.plot(res4a)
dense.plot(res4b)
dense.plot(res4c)
# }