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PAC (version 1.1.2)

Partition-Assisted Clustering and Multiple Alignments of Networks

Description

Implements partition-assisted clustering and multiple alignments of networks. It 1) utilizes partition-assisted clustering to find robust and accurate clusters and 2) discovers coherent relationships of clusters across multiple samples. It is particularly useful for analyzing single-cell data set. Please see Li et al. (2017) for detail method description.

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Version

Install

install.packages('PAC')

Monthly Downloads

282

Version

1.1.2

License

GPL-3

Maintainer

Ye Li

Last Published

March 20th, 2020

Functions in PAC (1.1.2)

PAC

Partition Assisted Clustering PAC 1) utilizes dsp or bsp-ll to recursively partition the data space and 2) applies a short round of kmeans style postprocessing to efficiently output clustered labels of data points.
getRepresentativeNetworks

Representative Networks
BSPLeaveCenter

Finds N Leaf centers in the data
JaccardSM

Calculates the Jaccard similarity matrix.
heatmapInput

Creates the matrix that can be easily plotted with a heatmap function available in an R package
annotateClades

Creates annotation matrix for the clades in aggregated format. The matrix contains average signals of each dimension for each clade in each sample
fmeasure

F-measure Calculation
constellationPlot

Makes constellation plot, in which the centroids are clusters are embedded in the t-SNE 2D plane and the cross-sample relationships are plotted as lines connecting related sample clusters (clades).
renamePrunedSubpopulations

Prune away specified subpopulations in clades that are far away.
annotationMatrix_withSubpopProp

Adds subpopulation proportion for the annotation matrix for the clades
runElbowPointAnalysis

Runs elbow point analysis to find the practical optimal number of clades to output. Outputs the average within sample cluster spread for all samples and the elbow point analysis plot with loess line fitted through the results.
getAverageSpreadOf2SubpopClades

Calculate the (global) average spread of subpopulations in clades with 2 subpopulations on the constellation plot.
getExtraneousCladeSubpopulations

Calculates subpopulations in clades (with two or more subpopulations) that are too far away from other subpopulations (within the same clade) on the constellation plot; these far away subpopulations should be pruned away from the original clades.
outputNetworks_topEdges_matrix

Wrapper to output the mutual information networks for subpopulations with size larger than a desired threshold.
MAN

Creates network alignments using network constructed from subpopulations after PAC
outputRepresentativeNetworks_topEdges

Outputs the representative/clade networks (plots and summary vectors) for subpopulations with size larger than a desired threshold. Saves the networks and the data matrices without the smaller subpopulations.
MINetworkPlot_topEdges

Plots mutual information network (mrnet algorithm) connection using the parmigene package. Mutual information calculated with infotheo package.
samplePass

Run PAC for Specified Samples
MINetwork_simplified_topEdges

Outputs the vectorized summary of a network based on the number of edges connected to a node
MINetwork_matrix_topEdges

Mutual information network connection matrix generation (mrnet algorithm) using the parmigene package. Mutual information calculated with infotheo package.
recordWithinClusterSpread

Calculates the within cluster spread
refineSubpopulationLabels

Refines the subpopulation labels from PAC using network alignment and small subpopulation information. Outputs a new set of files containing the representative labels.
aggregateData

Aggregates results from the clustering and merging step.