Using the PAC package

In PAC: Partition-Assisted Clustering and Multiple Alignments of Networks

This vignette illustrates the basic usage of the PAC package for R.

Biology Example

The PAC-MAN data analysis pipeline can be applied to mass-cytometry (CyTOF) data analysis. In this case, the user reads in the example data files (already saved as the Rdata format) subsetted from Bendall et al., 2011 and goes through the data analysis pipeline.

set.seed(1)

Load the required R packages

library(PAC)

Construct the sampleIDs vector to analyze the data

sampleIDs<-c("Basal", "BCR", "IL7")

Partition, cluster into desired number of subpopulations, and output subpopulation mutual information networks

samplePass(sampleIDs, dim_subset=NULL, hyperrectangles=35, num_PACSupop=25, num_networkEdge=25, max.iter=50)

Multiple Alignments of Networks

clades_network_only<-MAN(sampleIDs, num_PACSupop=25, smallSubpopCutoff=100, k_clades=5)

Refine the PAC labels with multiple alignments of networks representative labels for clades

refineSubpopulationLabels(sampleIDs,dim_subset=NULL, clades_network_only, expressionGroupClamp=5)

Draw clade/representative mutual information networks

getRepresentativeNetworks(sampleIDs, dim_subset=NULL, SubpopSizeFilter=200, num_networkEdge=25)

Obtain annotations of subpopulations

aggregateMatrix_withAnnotation<-annotateClades(sampleIDs, topHubs=4)
head(aggregateMatrix_withAnnotation)

Append subpopulation proportions for each sample in the annotation matrix

annotationMatrix_prop<-annotationMatrix_withSubpopProp(aggregateMatrix_withAnnotation)
head(annotationMatrix_prop)

PAC documentation built on Feb. 18, 2021, 9:06 a.m.