nudge.classify {DIME}

Classification Based on NUDGE Model

Description

Classifies observed data into differential and non-differential groups based on NUDGE model.

Usage

nudge.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 nudge.fit function.

obj.cutoff

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

obj.sigma.diff.cutoff

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

obj.mu.diff.cutoff

optional cut-off for standard deviation of the normal component in NUDGE model to be designated 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.

mu.diff.cutoff

normal component with mean > mu.diff.cutoff was used to represent differential component.

sigma.diff.cutoff

normal component with standard deviation > sigma.diff.cutoff was used to represent differential component.

Author(s)

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

See Also

nudge.fit

Examples

library(DIME);
# generate simulated datasets with underlying uniform and 1-normal components
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(1.5); rsigma <- c(1); 
rpi <- c(.10,.90); a <- (-6); b <- 6; 
chr1 <- c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
  rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]));
chr4 <- c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
  rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]));  
# analyzing chromosome 1 and 4
data <- list(chr1,chr4);

# fit NUDGE model with maximum iterations = 20 only
set.seed(1234);
test <- nudge.fit(data, max.iter=20)
# vector of classification. 1 represents differential, 0 denotes non-differential
nudgeClass <- test$class;