inudge.classify: Classification Based on iNUDGE Model
In DIME: Differential Identification using Mixture Ensemble

inudge.classify

R Documentation

Classification Based on iNUDGE Model

Description

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

Usage

inudge.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 `inudge.fit` function.
`obj.cutoff`	optional local fdr cutoff for classifying data into differential and non-differential groups based on iNUDGE model.
`obj.sigma.diff.cutoff`	optional cut-off for standard deviation of the normal component in iNUDGE model to be designated as representing differential.
`obj.mu.diff.cutoff`	optional cut-off for standard deviation of the normal component in iNUDGE 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 taslim.2@osu.edu, with contributions from Abbas Khalili khalili@stat.ubc.ca, Dustin Potter potterdp@gmail.com, and Shili Lin shili@stat.osu.edu

Examples

library(DIME);
# generate simulated datasets with underlying uniform and 2-normal distributions
set.seed(1234);
N1 <- 1500; N2 <- 500; rmu <- c(-2.25,1.5); rsigma <- c(1,1);
rpi <- c(.10,.45,.45); a <- (-6); b <- 6;
chr4 <- list(c(-runif(ceiling(rpi[1]*N1),min = a,max =b),
  rnorm(ceiling(rpi[2]*N1),rmu[1],rsigma[1]),
  rnorm(ceiling(rpi[3]*N1),rmu[2],rsigma[2])));
chr9 <- list(c(-runif(ceiling(rpi[1]*N2),min = a,max =b),
  rnorm(ceiling(rpi[2]*N2),rmu[1],rsigma[1]),
  rnorm(ceiling(rpi[3]*N2),rmu[2],rsigma[2])));
# analyzing chromosome 4 and 9
data <- list(chr4,chr9);

# fit iNUDGE model with 2 normal components and maximum iterations = 20
set.seed(1234);
test <- inudge.fit(data, K = 2, max.iter=20);
# vector of classification. 1 represents differential, 0 denotes non-differential
inudgeClass <- test$class;

DIME documentation built on May 9, 2022, 5:05 p.m.