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HTSCluster (version 1.1)

Clustering high throughput sequencing (HTS) data

Description

This package implements two parameterizations of a Poisson mixture model to cluster observations (e.g., genes) in high throughput sequencing data. Parameter estimation is performed using either the EM or CEM algorithm, and the BIC or ICL criteria are used for model selection (i.e., to choose the number of clusters).

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Version

Install

install.packages('HTSCluster')

Monthly Downloads

313

Version

1.1

License

GPL (>= 3)

Maintainer

Andrea Rau

Last Published

June 11th, 2012

Functions in HTSCluster (1.1)

PoisMixClus

Poisson mixture model estimation and model selection
PoisMixSim

Simulate data from a Poisson mixture model
PoisMixMean

Calculate the conditional per-cluster mean of each observation
summary.HTSCluster

Summarize results from clustering using a Poisson mixture model
HTSCluster-package

Clustering high throughput sequencing (HTS) data
plot.HTSCluster

Visualize results from clustering using a Poisson mixture model
Init

Small EM parameter initialization
logLikePoisMix

Calculate the log likelihood of a Poisson mixture model
probaPost

Calculate the conditional probability of belonging to each cluster in a Poisson mixture model