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IntClust (version 0.0.2)

Integrated Data Analysis via Clustering

Description

Several integrative data methods in which information of objects from different data sources can be combined are included in the IntClust package. As a single data source is limited in its point of view, this provides more insight and the opportunity to investigate how the variables are interconnected. Clustering techniques are to be applied to the combined information. For now, only agglomerative hierarchical clustering is implemented. Further, differential gene expression and pathway analysis can be conducted on the clusters. Plotting functions are available to visualize and compare results of the different methods.

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Version

Install

install.packages('IntClust')

Monthly Downloads

17

Version

0.0.2

License

GPL-3

Maintainer

Marijke Van Moerbeke

Last Published

March 31st, 2016

Functions in IntClust (0.0.2)

Aggregated Data Ensemble Clustering - version a

ContFeaturesPlot

Plot of continuous features

DetermineWeight_SimClust

Determines an optimal weight for weighted clustering by similarity weighted clustering.

Find an element in a data structure

Find a selection of compounds in the output of ReorderToReference

Aggregated Data Ensemble Clustering

Create a color palette to be used in the plots

Plot a dendrogram with leaves colored by a result of choice

A normalization function

PathwayAnalysis

Pathway Analysis

Aggregated Data Clustering

Intersection of clusters over multiple methods

SelectnrClusters

Determines an optimal number of clusters based on silhouette widths

Weighted clustering

Complementary Ensemble Clustering

BoxPlotDistance

Box plots of one distance matrix categorized against another distance matrix.

WeightedSimClust

Weighted similarity clustering

First example for colors

Second example for colors

A GO plot of a pathway analysis output.

Preparing a data set for pathway analysis

Similarity Network Fusion - version a

Complementary Ensemble Clustering - version a

Function that annotates colors to their names

Complementary Ensemble Clustering - version b

CompareInteractive

Interactive comparison of clustering results for a specific cluster or method.

Differential gene expressions for multiple results

Similarity Network Fusion - version b

IntClust-package

Integrated Data Analysis

FeaturesOfCluster

Lists all features present in a selected cluster of compounds

Investigates whether genes are differential expressed in multiple clusters

The fingerprint matrix for the MCF7 data

Geneset.intersect

Intersection over resulting gene sets of PathwaysIter function

List of GO Annotations

SharedGenesPathsFeat

Intersection of genes and pathways over multiple methods

SimilarityHeatmap

A heatmap of similarity values between compounds

SimilarityMeasure

A measure of similarity for the outputs of the different methods

Similarity Network Fusion

Interactive plot to determine DE Genes and DE features for a specific cluster

Aggregated Data Ensemble Clustering - version b

Perform clustering on a single data source

Comparison of clustering results for the single and multiple source clustering.

Comparison of clustering results over multiple results

The gene info data frame

The gene expression matrix

Plotting gene profiles

ReorderToReference

Order the outputs of the clustering methods against a reference

The target prediction matrix

Similarity Network Fusion - version c

CharacteristicFeatures

Determining the characteristic features of a cluster

Aggregated Data Ensemble Clustering - version c

BinFeaturesPlot

Plot of a selection of features

Pathway analysis for multiple clustering results

HeatmapSelection

A function to select a group of compounds via the similarity heatmap.

Comparing two clustering results with a heatmap

Complementary Ensemble Clustering - version c

Iterations of the pathway analysis

Function that performs any aggregated data function

Follow a cluster over multiple methods

Coloring specific leaves of a dendrogram

Weighting on Membership

Distance function

CompareSilCluster

Compares medoid clustering results based on silhouette widths

DetermineWeight_SilClust

Determines an optimal weight for weighted clustering by silhouettes widths.