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tileHMM (version 1.0-2)

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

1.0-2

License

GPL version 2 or newer

Maintainer

Peter Humburg

Last Published

February 15th, 2017

Functions in tileHMM (1.0-2)

initializeDist-methods

Generating Objects of Class 'dist'

Accessing Objects of Class "contHMM"

Accessing and Converting Objects of Class "dist"

Create HMM from Initial Parameter Estimates Obtained from Data

tileHMM-package

Hidden Markov Models for ChIP-on-Chip Analysis

Converting Information about Enriched Regions into GFF Format

Class "contHMM"

initializeHMM-methods

Generate Objects of Class 'hmm'

Efficient Estimation of HMM Parameters

Calculate Most Likely State Sequence Using the Viterbi Algorithm

Internal Functions

State Names of Hidden Markov Model

Plotting of "contDist" Objects

Calculate 'Shrinkage t' Statistic

viterbiTraining

Estimate HMM Parameters Using Viterbi Training

Extract Probe Calls from GFF File

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

Computation of Forward and Backward Variables

Generate Observation Sequence from HMM

Simulated ChIP-on-Chip Data

Baum-Welch Algorithm

Class "contDist"

Create HMM from Parameter Values

Generate Simulated Dataset

region.position

Identify Enriched Regions

Calculate Posterior Probability for States of HMM

Post-Processing of "tileHMM" Results

Sample Observations from Probability Distribution

Determine Length of Positive and Negative Regions

Class "discDist"