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.
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)
## Input Data: 2650 by 18
## Partition method: Discrepancy based partition
## Maximum level: 35
## partition completed
## [1] "Initial Clustering..."
## [1] "Merging..."
## Input Data: 3537 by 18
## Partition method: Discrepancy based partition
## Maximum level: 35
## partition completed
## [1] "Initial Clustering..."
## [1] "Merging..."
## Input Data: 3813 by 18
## Partition method: Discrepancy based partition
## Maximum level: 35
## partition completed
## [1] "Initial Clustering..."
## [1] "Merging..."
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)
## Annotation ClusterID SampleID pPLCgamma2 pSTAT5
## 1 pNFkB-pSTAT3-pH3-Ki67 clade1 Basal 0.868677090 1.4210433
## 2 IkBalpha-pP38-Ki67-pERK1.2 clade2 Basal 0.417650152 0.7512015
## 3 pNFkB-Ki67-pMAPKAPK2-pSHP2 clade3 Basal -0.009067766 0.1439336
## 4 pP38-pSrcFK-pSTAT3-pZAP70.Syk clade1 BCR 1.414513278 1.4852767
## 5 pSrcFK-pCREB-pSHP2-pSTAT3 clade2 BCR 0.331270112 0.7172607
## 6 pSrcFK-pNFkB-pBtk.Itk-Ki67 clade4 BCR 0.912350993 1.5864291
## Ki67 pSHP2 pERK1.2 pMAPKAPK2 pZAP70.Syk pSTAT3 pSLP
## 1 1.6045171 1.0443365 1.4661011 1.5628676 1.43355321 2.1289258 0.92427696
## 2 0.7591787 0.6073356 0.9891308 0.9838982 0.68705268 1.4899395 0.46984991
## 3 0.5970280 0.0675446 0.1806816 0.2359121 0.05943879 0.1938544 0.06218395
## 4 1.6055084 1.3134441 1.5878684 1.6812649 1.74982011 2.3305502 1.16167123
## 5 0.2062699 0.5510241 0.7372884 1.0448433 0.73709113 1.4776139 0.33423706
## 6 3.5343868 0.9396143 1.1513465 1.7927573 1.14747844 2.1468960 0.69638449
## pNFkB IkBalpha pH3 pP38 pBtk.Itk pS6 pSrcFK
## 1 2.5118414 1.930353 2.0931644 2.3369804 2.6054058 1.5696688 2.9274172
## 2 1.7826376 1.426169 0.9924652 1.5957836 1.7199279 0.6538710 1.8988397
## 3 0.4461451 0.206577 0.2719665 0.2130849 0.5528641 0.1526701 0.2939679
## 4 2.5682406 1.835245 2.6952983 2.8117867 2.5400931 1.8602256 3.1488323
## 5 1.7574310 1.692790 1.2809057 1.7151455 1.9010833 0.9869888 2.2340015
## 6 2.4373202 1.627335 2.1106601 2.3534429 5.0717325 2.3080344 2.1823834
## pCREB pCrkL count
## 1 1.80653721 1.03088589 1481
## 2 0.86030693 0.57582728 507
## 3 0.08732748 0.06956507 458
## 4 1.91621956 1.04601166 990
## 5 1.26005371 0.47350443 1341
## 6 1.48173361 0.66764625 437
Obtain heatmap input and plot heatmap
cladeProportionMatrix<-heatmapInput(aggregateMatrix_withAnnotation)
heatmap(as.matrix(cladeProportionMatrix))
Append subpopulation proportions for each sample in the annotation matrix
annotationMatrix_prop<-annotationMatrix_withSubpopProp(aggregateMatrix_withAnnotation)
head(annotationMatrix_prop)
## Annotation ClusterID SampleID pPLCgamma2 pSTAT5
## 1 pNFkB-pSTAT3-pH3-Ki67 clade1 Basal 0.868677090 1.4210433
## 2 IkBalpha-pP38-Ki67-pERK1.2 clade2 Basal 0.417650152 0.7512015
## 3 pNFkB-Ki67-pMAPKAPK2-pSHP2 clade3 Basal -0.009067766 0.1439336
## 4 pP38-pSrcFK-pSTAT3-pZAP70.Syk clade1 BCR 1.414513278 1.4852767
## 5 pSrcFK-pCREB-pSHP2-pSTAT3 clade2 BCR 0.331270112 0.7172607
## 6 pSrcFK-pNFkB-pBtk.Itk-Ki67 clade4 BCR 0.912350993 1.5864291
## Ki67 pSHP2 pERK1.2 pMAPKAPK2 pZAP70.Syk pSTAT3 pSLP
## 1 1.6045171 1.0443365 1.4661011 1.5628676 1.43355321 2.1289258 0.92427696
## 2 0.7591787 0.6073356 0.9891308 0.9838982 0.68705268 1.4899395 0.46984991
## 3 0.5970280 0.0675446 0.1806816 0.2359121 0.05943879 0.1938544 0.06218395
## 4 1.6055084 1.3134441 1.5878684 1.6812649 1.74982011 2.3305502 1.16167123
## 5 0.2062699 0.5510241 0.7372884 1.0448433 0.73709113 1.4776139 0.33423706
## 6 3.5343868 0.9396143 1.1513465 1.7927573 1.14747844 2.1468960 0.69638449
## pNFkB IkBalpha pH3 pP38 pBtk.Itk pS6 pSrcFK
## 1 2.5118414 1.930353 2.0931644 2.3369804 2.6054058 1.5696688 2.9274172
## 2 1.7826376 1.426169 0.9924652 1.5957836 1.7199279 0.6538710 1.8988397
## 3 0.4461451 0.206577 0.2719665 0.2130849 0.5528641 0.1526701 0.2939679
## 4 2.5682406 1.835245 2.6952983 2.8117867 2.5400931 1.8602256 3.1488323
## 5 1.7574310 1.692790 1.2809057 1.7151455 1.9010833 0.9869888 2.2340015
## 6 2.4373202 1.627335 2.1106601 2.3534429 5.0717325 2.3080344 2.1823834
## pCREB pCrkL count subpop_proportion
## 1 1.80653721 1.03088589 1481 60.55
## 2 0.86030693 0.57582728 507 20.73
## 3 0.08732748 0.06956507 458 18.72
## 4 1.91621956 1.04601166 990 29.43
## 5 1.26005371 0.47350443 1341 39.86
## 6 1.48173361 0.66764625 437 12.99