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tileHMM (version 1.0-5)

Hidden Markov Models for ChIP-on-Chip Analysis

Description

This package provides methods and classes to build HMMs that are suitable for the analysis of ChIP-on-chip data. The provided parameter estimation methods include the Baum-Welch algorithm and Viterbi training as well as a combination of both.

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Version

Install

install.packages('tileHMM')

Monthly Downloads

1

Version

1.0-5

License

GPL (>= 2)

Maintainer

Peter Humburg

Last Published

March 19th, 2012

Functions in tileHMM (1.0-5)

Generate Simulated Dataset

Internal Functions

Create HMM from Parameter Values

Plotting of "contDist" Objects

tileHMM-package

Hidden Markov Models for ChIP-on-Chip Analysis

Calculate 'Shrinkage t' Statistic

Class "discDist"

Class "contDist"

viterbiTraining

Estimate HMM Parameters Using Viterbi Training

initializeDist-methods

Generating Objects of Class 'dist'

Accessing and Converting Objects of Class "dist"

Class "contHMM"

Computation of Forward and Backward Variables

initializeHMM-methods

Generate Objects of Class 'hmm'

Converting Information about Enriched Regions into GFF Format

Calculate Most Likely State Sequence Using the Viterbi Algorithm

Post-Processing of "tileHMM" Results

Calculate log(x + y) from log(x) and log(y)

Create HMM from Initial Parameter Estimates Obtained from Data

Sample Observations from Probability Distribution

Baum-Welch Algorithm

Generate Observation Sequence from HMM

Determine Length of Positive and Negative Regions

Simulated ChIP-on-Chip Data

Efficient Estimation of HMM Parameters

region.position

Identify Enriched Regions

Accessing Objects of Class "contHMM"

Calculate Posterior Probability for States of HMM

State Names of Hidden Markov Model

Extract Probe Calls from GFF File