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