DIME (version 1.3.0)

gng.plot.comp: Plot GNG Individual Components

Description

Plot each estimated individual components of GNG model (mixture of exponential and k-normal) fitted using gng.fit.

Usage

gng.plot.comp(data, obj, new.plot = TRUE, legpos = NULL, xlim=NULL,
  ylim=NULL, xlab=NULL, ylab=NULL, main=NULL,lwd=NULL,...)

Arguments

data

an R list of vector of normalized intensities (counts). Each element can correspond to a particular chromosome. User can construct their own list containing only the chromosome(s) they want to analyze.

obj

a list object returned by gng.fit function.

new.plot

optional logical variable on whether to create new plot.

legpos

optional vector of (x,y) location for the legend position

xlim

optional x-axis limit (see par).

ylim

optional y-axis limit (see par).

xlab

optional x-axis label (see par).

ylab

optional y-axis label (see par).

main

optional plot title (see par).

lwd

optional line width for lines in the plot (see par).

…

additional graphical arguments to be passed to methods (see par).

Details

The components representing differential data are denoted by asterisk (*) symbol on the plot legend.

See Also

gng.plot.mix, gng.plot.comp, gng.plot.fit, gng.plot.qq, DIME.plot.fit, inudge.plot.fit.

Examples

# NOT RUN {
library(DIME);
# generate simulated datasets with underlying exponential-normal components
N1 <- 1500; N2 <- 500; K <- 4; rmu <- c(-2.25,1.50); rsigma <- c(1,1); 
rpi <- c(.05,.45,.45,.05); rbeta <- c(12,10);
set.seed(1234);
chr1 <- c(-rgamma(ceiling(rpi[1]*N1),shape = 1,scale = rbeta[1]), 
  rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]), 
  rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2]), 
  rgamma(ceiling(rpi[4]*N1),shape = 1,scale = rbeta[2]));
chr2 <- c(-rgamma(ceiling(rpi[1]*N2),shape = 1,scale = rbeta[1]), 
  rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), 
  rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]), 
  rgamma(ceiling(rpi[4]*N2),shape = 1,scale = rbeta[2])); 
chr3 <- c(-rgamma(ceiling(rpi[1]*N2),shape = 1,scale = rbeta[1]), 
  rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]), 
  rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2]), 
  rgamma(ceiling(rpi[4]*N2),shape = 1,scale = rbeta[2]));
# analyzing only chromosome 1 and chromosome 3
data <- list(chr1,chr3);

# Fitting a GNG model with 2-normal component
bestGng <- gng.fit(data,K=2);

# plot individual components of GNG
gng.plot.comp(data,bestGng);
# plot mixture component on top of the individual components plot
gng.plot.mix(bestGng,resolution=1000,new.plot=FALSE);

# }