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IntClust (version 0.1.0)

Integration of Multiple Data Sets with Clustering Techniques

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

0.1.0

License

GPL-3

Maintainer

Marijke Van Moerbeke

Last Published

July 30th, 2018

Functions in IntClust (0.1.0)

ADEC

Aggregated data ensemble clustering
ColorsNames

Function that annotates colors to their names
BoxPlotDistance

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

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

Consensus clustering
CompareSvsM

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

Information of the genes Gene info in a data frame
GS

List of GO Annotations
ComparePlot

Comparison of clustering results over multiple results
Cluster

Single source clustering
CompareSilCluster

Compares medoid clustering results based on silhouette widths
PlotPathways

A GO plot of a pathway analysis output.
ClusterCols

Matching clusters with colours
PreparePathway

Preparing a data set for pathway analysis
ColorPalette

Create a color palette to be used in the plots
ClusterPlot

Colouring clusters in a dendrogram
DiffGenesSelection

Differential expression for a selection of objects
FindElement

Find an element in a data structure
ClusteringAggregation

Clustering aggregation
ContFeaturesPlot

Plot of continuous features
Distance

Distance calculation
EHC

Ensemble for hierarchical clustering
HeatmapPlot

Comparing two clustering results with a heatmap
HBGF

Hybrid bipartite graph formulation
EnsembleClustering

Ensemble clustering
DetermineWeight_SilClust

Determines an optimal weight for weighted clustering by silhouettes widths.
FindGenes

Investigates whether genes are differential expressed in multiple clusters
SharedGenesPathsFeat

Intersection of genes and pathways over multiple methods
SharedComps

Intersection of clusters across multiple methods
Colors1

Colour examples
FeaturesOfCluster

List all features present in a selected cluster of objects
M_ABC

Multi-source ABC clustering
Normalization

Normalization of features
FeatSelection

feature selection for a selection of objects
EvidenceAccumulation

Evidence accumulation
LabelPlot

Coloring specific leaves of a dendrogram
FindCluster

Find a selection of objects in the output of ReorderToReference
PathwaysIter

Iterations of the pathway analysis
LinkBasedClustering

Link based clustering
DetermineWeight_SimClust

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

Differential gene expressions for multiple results
PathwayAnalysis

Pathway Analysis
Geneset.intersectSelection

Intersection over resulting gene sets of PathwaysIter function for a selection of objects
IntClust

Integrated Clustering Methods.
Geneset.intersect

Intersection over resulting gene sets of PathwaysIter function
Pathways

Pathway analysis for multiple clustering results
SNF

Similarity network fusion
LabelCols

Colouring labels
PathwaysSelection

Pathway analysis for a selection of objects
HeatmapSelection

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

Intersection of pathways over multiple methods for a selection of objects.
SelectnrClusters

Determines an optimal number of clusters based on silhouette widths
SimilarityMeasure

A measure of similarity for the outputs of the different methods
TrackCluster

Follow a cluster over multiple methods
distanceheatmaps

Determine the distance in a heatmap
f.clustABC.MultiSource

f.clustABC.MultiSoucre
SimilarityHeatmap

A heatmap of similarity values between objects
HierarchicalEnsembleClustering

Hierarchical ensemble clustering
ProfilePlot

Plotting gene profiles
SharedSelection

Intersection of genes and pathways over multiple methods for a selection of objects.
ReorderToReference

Order the outputs of the clustering methods against a reference
WeightedClust

Weighted clustering
SharedSelectionLimma

Intersection of genes over multiple methods for a selection of objects.
WonM

Weighting on membership
f.t

ff
f.gsample

f.gsample
f.rmv

f.rmv
fingerprintMat

Fingerprint data
geneMat

Gene expression data
targetMat

Target prediction data
CharacteristicFeatures

Determining the characteristic features of a cluster
ChooseCluster

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

Complementary ensemble clustering
CVAA

Cumulative voting-based aggregation algorithm
BinFeaturesPlot_SingleData

Visualization of characteristic binary features of a single data set
ABC.SingleInMultiple

Single-source ABC clustering
BinFeaturesPlot_MultipleData

Visualization of characteristic binary features of multiple data sets
ADC

Aggregated data clustering