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.
|Author||Marijke Van Moerbeke|
|Date of publication||2016-02-15 07:51:46|
|Maintainer||Marijke Van Moerbeke <firstname.lastname@example.org>|
ADC: Aggregated Data Clustering
ADEC: Aggregated Data Ensemble Clustering
ADECa: Aggregated Data Ensemble Clustering - version a
ADECb: Aggregated Data Ensemble Clustering - version b
ADECc: Aggregated Data Ensemble Clustering - version c
BinFeaturesPlot: Plot of a selection of features
BoxPlotDistance: Box plots of one distance matrix categorized against another...
CEC: Complementary Ensemble Clustering
CECa: Complementary Ensemble Clustering - version a
CECb: Complementary Ensemble Clustering - version b
CECc: Complementary Ensemble Clustering - version c
CharacteristicFeatures: Determining the characteristic features of a cluster
ChooseCluster: Interactive plot to determine DE Genes and DE features for a...
Cluster: Perform clustering on a single data source
ClusterPlot: Plot a dendrogram with leaves colored by a result of choice
Colorpalette: Create a color palette to be used in the plots
Colors1: First example for colors
Colors2: Second example for colors
ColorsNames: Function that annotates colors to their names
CompareInteractive: Interactive comparison of clustering results for a specific...
ComparePlot: Comparison of clustering results over multiple results
CompareSilCluster: Compares medoid clustering results based on silhouette widths
CompareSvsM: Comparison of clustering results for the single and multiple...
ContFeaturesPlot: Plot of continuous features
DetermineWeight_SilClust: Determines an optimal weight for weighted clustering by...
DetermineWeight._SimClust: Determines an optimal weight for weighted clustering by...
DiffGenes: Differential gene expressions for multiple results
Distance: Distance function
FeaturesOfCluster: Lists all features present in a selected cluster of compounds
FindCluster: Find a selection of compounds in the output of...
FindElement: Find an element in a data structure
FindGenes: Investigates whether genes are differential expressed in...
fingerprintMat: The fingerprint matrix for the MCF7 data
GeneInfo: The gene info data frame
geneMat: The gene expression matrix
Geneset.intersect: Intersection over resulting gene sets of 'PathwaysIter'...
GS: List of GO Annotations
HeatmapPlot: Comparing two clustering results with a heatmap
HeatmapSelection: A function to select a group of compounds via the similarity...
IntClust-package: Integrated Data Analysis
LabelPlot: Coloring specific leaves of a dendrogram
Normalization: A normalization function
PathwayAnalysis: Pathway Analysis
Pathways: Pathway analysis for multiple clustering results
PathwaysIter: Iterations of the pathway analysis
PlotPathways: A GO plot of a pathway analysis output.
PreparePathway: Preparing a data set for pathway analysis
ProfilePlot: Plotting gene profiles
ReorderToReference: Order the outputs of the clustering methods against a...
SelectnrClusters: Determines an optimal number of clusters based on silhouette...
SharedComps: Intersection of clusters over multiple methods
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...
SNF: Similarity Network Fusion
SNFa: Similarity Network Fusion - version a
SNFb: Similarity Network Fusion - version b
SNFc: Similarity Network Fusion - version c
targetMat: The target prediction matrix
TrackCluster: Follow a cluster over multiple methods
Ultimate: Function that performs any aggregated data function
WeightedClust: Weighted clustering
WeightedSimClust: Weighted similarity clustering
WonM: Weighting on Membership