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Clusternomics

Integrative clustering for heterogeneous datasets.

Introduction

The goal of context-dependendent clustering is to identify clusters in a set of related datasets. Clusternomics identifies both local clusters that exist at the level of individual datasets, and global clusters that appear across the datasets.

A typical application of the method is the task of cancer subtyping, where we analyse tumour samples. The individual datasets (contexts) are then various features of the tumour samples, such as gene expression data, DNA methylation measurements, miRNA expression etc. The assumption is that we have several measurements of different types describing the same set tumours. Each of the measurements then describes the tumour in a different context.

The clusternomics algorithm identifies

  • clusters of measurements within individual datasets, we call these local clusters
  • clusters of tumour samples that are informed by the local clusters, these are global clusters

The following diagram illustrates the distinction. When we look at the data sets individually, context 1 contains three clusters and context 2 contains two clusters. These clusters correspond to the local clusters in the clusternomics package. On the global level, there are three distinct clusters that are only revealed when we look at the combination of local assignments within individual datasets.

Installation

Use the devtools package to get the current version of the package:

devtools::install_github("evelinag/clusternomics")

Using clusternomics

See the package vignette for usage on a simulated dataset.

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Install

install.packages('clusternomics')

Monthly Downloads

477

Version

0.1.0

License

MIT + file LICENSE

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Last Published

August 1st, 2016

Functions in clusternomics (0.1.0)

empiricalBayesPrior

Fit an empirical Bayes prior to the data
generateTestData_1D

Generate simulated 1D dataset for testing
generatePrior

Generate a basic prior distribution for the datasets.
generateTestData_2D

Generate simulated 2D dataset for testing
clusterSizes

Estimate sizes of clusters from global cluster assignments.
contextCluster

Clusternomics: Context-dependent clustering
coclusteringMatrix

Compute the posterior co-clustering matrix from global cluster assignments.
numberOfClusters

Estimate number of clusters from global cluster assignments.