Partition-Assisted Clustering and Multiple Alignments of Networks
Aggregates results from the clustering and merging step.
Creates annotation matrix for the clades in aggregated format. The mat...
Adds subpopulation proportion for the annotation matrix for the clades
Finds N Leaf centers in the data
Makes constellation plot, in which the centroids are clusters are embe...
F-measure Calculation
Calculate the (global) average spread of subpopulations in clades with...
Calculates subpopulations in clades (with two or more subpopulations) ...
Representative Networks
Creates the matrix that can be easily plotted with a heatmap function ...
Calculates the Jaccard similarity matrix.
Creates network alignments using network constructed from subpopulatio...
Mutual information network connection matrix generation (mrnet algorit...
Outputs the vectorized summary of a network based on the number of edg...
Plots mutual information network (mrnet algorithm) connection using th...
Wrapper to output the mutual information networks for subpopulations w...
Outputs the representative/clade networks (plots and summary vectors) ...
Partition Assisted Clustering PAC 1) utilizes dsp or bsp-ll to recursi...
Calculates the within cluster spread
Refines the subpopulation labels from PAC using network alignment and ...
Prune away specified subpopulations in clades that are far away.
Runs elbow point analysis to find the practical optimal number of clad...
Run PAC for Specified Samples
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) <doi:10.1371/journal.pcbi.1005875> for detail method description.