gng.plot.comp {DIME}

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.

Author(s)

Cenny Taslim [e-mail], with contributions from Abbas Khalili [e-mail], Dustin Potter [e-mail], and Shili Lin [e-mail]

See Also

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

Examples

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);