DIME.classify {DIME}

Classification Based on The Best Model

Description

Classifies observed data into differential and non-differential groups based on the model selected as the best fit to the observed data.

Usage

DIME.classify(data, obj, obj.cutoff = 0.1, obj.sigma.diff.cutoff = NULL, 
  obj.mu.diff.cutoff = NULL)

Arguments

data

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

obj

a list object returned by DIME function.

obj.cutoff

optional local fdr cutoff for classifying data into differential and non-differential groups based on the best mixture model.

obj.sigma.diff.cutoff

optional cut-off for standard deviation of the normal component in the best model to be declared as representing differential.

obj.mu.diff.cutoff

optional cut-off for standard deviation of the normal component in the best model to be declared as representing differential.

Value

A list object passed as input with additional element $class containing vector of classifications for all the observations in data. A classification of 1 denotes that the data is classified as differential with fdr < obj.cutoff.

Author(s)

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

See Also

DIME

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);
 
# run DIME with small maximum iteration and repetitions
set.seed(1234);
test <- DIME(data,gng.max.iter=10,gng.rep=1,inudge.max.iter=10,inudge.rep=1,
 nudge.max.iter=10,nudge.rep=1)
# get classification based on inudge  
test$inudge <- DIME.classify(data,test$inudge,obj.cutoff=0.1);
# vector of classification. 1 represents differential, 0 denotes non-differential
inudgeClass <- test$inudge$class;