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HTSCluster (version 2.0.3)

Clustering high throughput sequencing (HTS) data

Description

This package implements 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 ICL criterion is 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

2.0.3

License

GPL (>= 3)

Maintainer

Andrea Rau

Last Published

July 28th, 2014

Functions in HTSCluster (2.0.3)

probaPost

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

Simulate data from a Poisson mixture model
PoisMixMean

Calculate the conditional per-cluster mean of each observation
logLikePoisMixDiff

Calculate the difference in log likelihood of two Poisson mixture models or the log-likelihood for each observation
plot.HTSCluster

Visualize results from clustering using a Poisson mixture model
summary.HTSCluster

Summarize results from clustering using a Poisson mixture model
PoisMixClus

Poisson mixture model estimation and model selection
Init

Parameter initialization for a Poisson mixture model.
HTSCluster-package

Clustering high throughput sequencing (HTS) data
highDimensionARI

Calculate ARI for high-dimensional data via data splits