| Type: | Package |
| Title: | Integrative Clustering for Heterogeneous Biomedical Datasets |
| Version: | 0.1.1 |
| Author: | Evelina Gabasova |
| Maintainer: | Evelina Gabasova <egabasova@gmail.com> |
| Description: | Integrative context-dependent clustering for heterogeneous biomedical datasets. Identifies local clustering structures in related datasets, and a global clusters that exist across the datasets. |
| License: | MIT + file LICENSE |
| LazyData: | TRUE |
| Imports: | magrittr, plyr, MASS |
| Suggests: | knitr, rmarkdown, testthat, mclust, gplots, ggplot2 |
| URL: | https://github.com/evelinag/clusternomics |
| BugReports: | https://github.com/evelinag/clusternomics/issues |
| RoxygenNote: | 5.0.1 |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2017-03-14 13:54:54 UTC; evelina |
| Repository: | CRAN |
| Date/Publication: | 2017-03-14 15:13:04 |
Estimate sizes of clusters from global cluster assignments.
Description
Estimate sizes of clusters from global cluster assignments.
Usage
clusterSizes(assignments)
Arguments
assignments |
Matrix of cluster assignments, where each row corresponds to cluster assignments sampled in one MCMC iteration |
Value
Sizes of individual clusters in each MCMC iteration.
Examples
# Generate simple test dataset
groupCounts <- c(50, 10, 40, 60)
means <- c(-1.5,1.5)
testData <- generateTestData_2D(groupCounts, means)
datasets <- testData$data
# Fit the model
# 1. specify number of clusters
clusterCounts <- list(global=10, context=c(3,3))
# 2. Run inference
# Number of iterations is just for demonstration purposes, use
# a larger number of iterations in practice!
results <- contextCluster(datasets, clusterCounts,
maxIter = 10, burnin = 5, lag = 1,
dataDistributions = 'diagNormal',
verbose = TRUE)
# Extract only the sampled global assignments
samples <- results$samples
clusters <- plyr::laply(1:length(samples), function(i) samples[[i]]$Global)
clusterSizes(clusters)
Compute the posterior co-clustering matrix from global cluster assignments.
Description
Compute the posterior co-clustering matrix from global cluster assignments.
Usage
coclusteringMatrix(assignments)
Arguments
assignments |
Matrix of cluster assignments, where each row corresponds to cluster assignments sampled in one MCMC iteration |
Value
Posterior co-clustering matrix, where element [i,j] represents
the posterior probability that data points i and j belong
to the same cluster.
Examples
# Generate simple test dataset
groupCounts <- c(50, 10, 40, 60)
means <- c(-1.5,1.5)
testData <- generateTestData_2D(groupCounts, means)
datasets <- testData$data
# Fit the model
# 1. specify number of clusters
clusterCounts <- list(global=10, context=c(3,3))
# 2. Run inference
# Number of iterations is just for demonstration purposes, use
# a larger number of iterations in practice!
results <- contextCluster(datasets, clusterCounts,
maxIter = 10, burnin = 5, lag = 1,
dataDistributions = 'diagNormal',
verbose = TRUE)
# Extract only the sampled global assignments
samples <- results$samples
clusters <- plyr::laply(1:length(samples), function(i) samples[[i]]$Global)
coclusteringMatrix(clusters)
Clusternomics: Context-dependent clustering
Description
This function fits the context-dependent clustering model to the data using Gibbs sampling. It allows the user to specify a different number of clusters on the global level, as well as on the local level.
Usage
contextCluster(datasets, clusterCounts, dataDistributions = "diagNormal",
prior = NULL, maxIter = 1000, burnin = NULL, lag = 3,
verbose = FALSE)
Arguments
datasets |
List of data matrices where each matrix represents a context-specific dataset. Each data matrix has the size N times M, where N is the number of data points and M is the dimensionality of the data. The full list of matrices has length C. The number of data points N must be the same for all data matrices. |
clusterCounts |
Number of cluster on the global level and in each context.
List with the following structure: |
dataDistributions |
Distribution of data in each dataset. Can be either a list of
length C where |
prior |
Prior distribution. If |
maxIter |
Number of iterations of the Gibbs sampling algorithm. |
burnin |
Number of burn-in iterations that will be discarded. If not specified,
the algorithm discards the first half of the |
lag |
Used for thinning the samples. |
verbose |
Print progress, by default |
Value
Returns list containing the sequence of MCMC states and the log likelihoods of the individual states.
samples |
List of assignments sampled from the posterior,
each state |
logliks |
Log likelihoods during MCMC iterations. |
DIC |
Deviance information criterion to help select the number of clusters. Lower values of DIC correspond to better-fitting models. |
Examples
# Example with simulated data (see vignette for details)
# Number of elements in each cluster
groupCounts <- c(50, 10, 40, 60)
# Centers of clusters
means <- c(-1.5,1.5)
testData <- generateTestData_2D(groupCounts, means)
datasets <- testData$data
# Fit the model
# 1. specify number of clusters
clusterCounts <- list(global=10, context=c(3,3))
# 2. Run inference
# Number of iterations is just for demonstration purposes, use
# a larger number of iterations in practice!
results <- contextCluster(datasets, clusterCounts,
maxIter = 10, burnin = 5, lag = 1,
dataDistributions = 'diagNormal',
verbose = TRUE)
# Extract results from the samples
# Final state:
state <- results$samples[[length(results$samples)]]
# 1) assignment to global clusters
globalAssgn <- state$Global
# 2) context-specific assignmnets- assignment in specific dataset (context)
contextAssgn <- state[,"Context 1"]
# Assess the fit of the model with DIC
results$DIC
Fit an empirical Bayes prior to the data
Description
Fit an empirical Bayes prior to the data
Usage
empiricalBayesPrior(datasets, distributions = "diagNormal",
globalConcentration = 0.1, localConcentration = 0.1, type = "fitRate")
Arguments
datasets |
List of data matrices where each matrix represents a context-specific dataset. Each data matrix has the size N times M, where N is the number of data points and M is the dimensionality of the data. The full list of matrices has length C. The number of data points N must be the same for all data matrices. |
distributions |
Distribution of data in each dataset. Can be either a list of
length C where |
globalConcentration |
Prior concentration parameter for the global clusters. Small values of this parameter give larger prior probability to smaller number of clusters. |
localConcentration |
Prior concentration parameter for the local context-specific clusters. Small values of this parameter give larger prior probability to smaller number of clusters. |
type |
Type of prior that is fitted to the data. The algorithm can fit either rate of the prior covariance matrix, or fit the full covariance matrix to the data. |
Value
Returns the prior object that can be used as an input for the contextCluster
function.
Examples
# Example with simulated data (see vignette for details)
nContexts <- 2
# Number of elements in each cluster
groupCounts <- c(50, 10, 40, 60)
# Centers of clusters
means <- c(-1.5,1.5)
testData <- generateTestData_2D(groupCounts, means)
datasets <- testData$data
# Generate the prior
fullDataDistributions <- rep('diagNormal', nContexts)
prior <- empiricalBayesPrior(datasets, fullDataDistributions, 0.01, 0.1, 'fitRate')
# Fit the model
# 1. specify number of clusters
clusterCounts <- list(global=10, context=c(3,3))
# 2. Run inference
# Number of iterations is just for demonstration purposes, use
# a larger number of iterations in practice!
results <- contextCluster(datasets, clusterCounts,
maxIter = 10, burnin = 5, lag = 1,
dataDistributions = 'diagNormal', prior = prior,
verbose = TRUE)
Generate a basic prior distribution for the datasets.
Description
Creates a basic prior distribution for the clustering model, assuming a unit prior covariance matrix for clusters in each dataset.
Usage
generatePrior(datasets, distributions = "diagNormal",
globalConcentration = 0.1, localConcentration = 0.1)
Arguments
datasets |
List of data matrices where each matrix represents a context-specific dataset. Each data matrix has the size N times M, where N is the number of data points and M is the dimensionality of the data. The full list of matrices has length C. The number of data points N must be the same for all data matrices. |
distributions |
Distribution of data in each dataset. Can be either a list of
length C where |
globalConcentration |
Prior concentration parameter for the global clusters. Small values of this parameter give larger prior probability to smaller number of clusters. |
localConcentration |
Prior concentration parameter for the local context-specific clusters. Small values of this parameter give larger prior probability to smaller number of clusters. |
Value
Returns the prior object that can be used as an input for the contextCluster
function.
Examples
# Example with simulated data (see vignette for details)
nContexts <- 2
# Number of elements in each cluster
groupCounts <- c(50, 10, 40, 60)
# Centers of clusters
means <- c(-1.5,1.5)
testData <- generateTestData_2D(groupCounts, means)
datasets <- testData$data
# Generate the prior
fullDataDistributions <- rep('diagNormal', nContexts)
prior <- generatePrior(datasets, fullDataDistributions, 0.01, 0.1)
# Fit the model
# 1. specify number of clusters
clusterCounts <- list(global=10, context=c(3,3))
# 2. Run inference
# Number of iterations is just for demonstration purposes, use
# a larger number of iterations in practice!
results <- contextCluster(datasets, clusterCounts,
maxIter = 10, burnin = 5, lag = 1,
dataDistributions = 'diagNormal', prior = prior,
verbose = TRUE)
Generate simulated 1D dataset for testing
Description
Generate simple 1D dataset with two contexts, where the data are generated from Gaussian distributions. The generated output contains two datasets, where each dataset contains 4 global clusters, originating from two local clusters in each context.
Usage
generateTestData_1D(groupCounts, means)
Arguments
groupCounts |
Number of data samples in each global cluster.
It is assumed to be a vector of four elements: |
means |
Means of the simulated clusters.
It is assumed to be a vector of two elements: |
Value
Returns the simulated datasets together with true assignmets.
data |
List of datasets for each context. This can be used as an input
for the |
groups |
True cluster assignments that were used to generate the data. |
Examples
groupCounts <- c(50, 10, 40, 60)
means <- c(-1.5,1.5)
testData <- generateTestData_1D(groupCounts, means)
# Use the dataset as an input for the contextCluster function for testing
datasets <- testData$data
Generate simulated 2D dataset for testing
Description
Generate simple 2D dataset with two contexts, where the data are generated from Gaussian distributions. The generated output contains two datasets, where each dataset contains 4 global clusters, originating from two local clusters in each context.
Usage
generateTestData_2D(groupCounts, means, variances = NULL)
Arguments
groupCounts |
Number of data samples in each global cluster.
It is assumed to be a vector of four elements: |
means |
Means of the simulated clusters.
It is assumed to be a vector of two elements: |
variances |
Optionally, it is possible to specify different variance
for each of the clusters. The variance is assumed to be a vector
of two elements: |
Value
Returns the simulated datasets together with true assignmets.
data |
List of datasets for each context. This can be used as an input
for the |
groups |
True cluster assignments that were used to generate the data. |
Examples
groupCounts <- c(50, 10, 40, 60)
means <- c(-1.5,1.5)
testData <- generateTestData_1D(groupCounts, means)
# Use the dataset as an input for the contextCluster function for testing
datasets <- testData$data
Estimate number of clusters from global cluster assignments.
Description
Estimate number of clusters from global cluster assignments.
Usage
numberOfClusters(assignments)
Arguments
assignments |
Matrix of cluster assignments, where each row corresponds to cluster assignments sampled in one MCMC iteration |
Value
Number of unique clusters in each MCMC iteration.
Examples
# Generate simple test dataset
groupCounts <- c(50, 10, 40, 60)
means <- c(-1.5,1.5)
testData <- generateTestData_2D(groupCounts, means)
datasets <- testData$data
# Fit the model
# 1. specify number of clusters
clusterCounts <- list(global=10, context=c(3,3))
# 2. Run inference
# Number of iterations is just for demonstration purposes, use
# a larger number of iterations in practice!
results <- contextCluster(datasets, clusterCounts,
maxIter = 10, burnin = 5, lag = 1,
dataDistributions = 'diagNormal',
verbose = TRUE)
# Extract only the sampled global assignments
samples <- results$samples
clusters <- plyr::laply(1:length(samples), function(i) samples[[i]]$Global)
numberOfClusters(clusters)