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
Construct the sampleIDs vector to analyze the data
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..."## Warning: `funs()` was deprecated in dplyr 0.8.0.
## ℹ Please use a list of either functions or lambdas:
##
## # Simple named list: list(mean = mean, median = median)
##
## # Auto named with `tibble::lst()`: tibble::lst(mean, median)
##
## # Using lambdas list(~ mean(., trim = .2), ~ median(., na.rm = TRUE))
## ℹ The deprecated feature was likely used in the PAC package.
## Please report the issue to the authors.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.## 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
Refine the PAC labels with multiple alignments of networks representative labels for clades
Draw clade/representative mutual information networks
Obtain annotations of subpopulations
aggregateMatrix_withAnnotation<-annotateClades(sampleIDs, topHubs=4)
head(aggregateMatrix_withAnnotation)## Annotation ClusterID SampleID pPLCgamma2 pSTAT5 Ki67 pSHP2
## 1 clade1 Basal 0.868677090 1.4210433 1.6045171 1.0443365
## 2 clade2 Basal 0.417650152 0.7512015 0.7591787 0.6073356
## 3 clade3 Basal -0.009067766 0.1439336 0.5970280 0.0675446
## 4 clade1 BCR 1.414513278 1.4852767 1.6055084 1.3134441
## 5 clade2 BCR 0.331270112 0.7172607 0.2062699 0.5510241
## 6 clade4 BCR 0.912350993 1.5864291 3.5343868 0.9396143
## pERK1.2 pMAPKAPK2 pZAP70.Syk pSTAT3 pSLP pNFkB IkBalpha
## 1 1.4661011 1.5628676 1.43355321 2.1289258 0.92427696 2.5118414 1.930353
## 2 0.9891308 0.9838982 0.68705268 1.4899395 0.46984991 1.7826376 1.426169
## 3 0.1806816 0.2359121 0.05943879 0.1938544 0.06218395 0.4461451 0.206577
## 4 1.5878684 1.6812649 1.74982011 2.3305502 1.16167123 2.5682406 1.835245
## 5 0.7372884 1.0448433 0.73709113 1.4776139 0.33423706 1.7574310 1.692790
## 6 1.1513465 1.7927573 1.14747844 2.1468960 0.69638449 2.4373202 1.627335
## pH3 pP38 pBtk.Itk pS6 pSrcFK pCREB pCrkL count
## 1 2.0931644 2.3369804 2.6054058 1.5696688 2.9274172 1.80653721 1.03088589 1481
## 2 0.9924652 1.5957836 1.7199279 0.6538710 1.8988397 0.86030693 0.57582728 507
## 3 0.2719665 0.2130849 0.5528641 0.1526701 0.2939679 0.08732748 0.06956507 458
## 4 2.6952983 2.8117867 2.5400931 1.8602256 3.1488323 1.91621956 1.04601166 990
## 5 1.2809057 1.7151455 1.9010833 0.9869888 2.2340015 1.26005371 0.47350443 1341
## 6 2.1106601 2.3534429 5.0717325 2.3080344 2.1823834 1.48173361 0.66764625 437Append subpopulation proportions for each sample in the annotation matrix
annotationMatrix_prop<-annotationMatrix_withSubpopProp(aggregateMatrix_withAnnotation)
head(annotationMatrix_prop)## Annotation ClusterID SampleID pPLCgamma2 pSTAT5 Ki67 pSHP2
## 1 clade1 Basal 0.868677090 1.4210433 1.6045171 1.0443365
## 2 clade2 Basal 0.417650152 0.7512015 0.7591787 0.6073356
## 3 clade3 Basal -0.009067766 0.1439336 0.5970280 0.0675446
## 4 clade1 BCR 1.414513278 1.4852767 1.6055084 1.3134441
## 5 clade2 BCR 0.331270112 0.7172607 0.2062699 0.5510241
## 6 clade4 BCR 0.912350993 1.5864291 3.5343868 0.9396143
## pERK1.2 pMAPKAPK2 pZAP70.Syk pSTAT3 pSLP pNFkB IkBalpha
## 1 1.4661011 1.5628676 1.43355321 2.1289258 0.92427696 2.5118414 1.930353
## 2 0.9891308 0.9838982 0.68705268 1.4899395 0.46984991 1.7826376 1.426169
## 3 0.1806816 0.2359121 0.05943879 0.1938544 0.06218395 0.4461451 0.206577
## 4 1.5878684 1.6812649 1.74982011 2.3305502 1.16167123 2.5682406 1.835245
## 5 0.7372884 1.0448433 0.73709113 1.4776139 0.33423706 1.7574310 1.692790
## 6 1.1513465 1.7927573 1.14747844 2.1468960 0.69638449 2.4373202 1.627335
## pH3 pP38 pBtk.Itk pS6 pSrcFK pCREB pCrkL count
## 1 2.0931644 2.3369804 2.6054058 1.5696688 2.9274172 1.80653721 1.03088589 1481
## 2 0.9924652 1.5957836 1.7199279 0.6538710 1.8988397 0.86030693 0.57582728 507
## 3 0.2719665 0.2130849 0.5528641 0.1526701 0.2939679 0.08732748 0.06956507 458
## 4 2.6952983 2.8117867 2.5400931 1.8602256 3.1488323 1.91621956 1.04601166 990
## 5 1.2809057 1.7151455 1.9010833 0.9869888 2.2340015 1.26005371 0.47350443 1341
## 6 2.1106601 2.3534429 5.0717325 2.3080344 2.1823834 1.48173361 0.66764625 437
## subpop_proportion
## 1 60.55
## 2 20.73
## 3 18.72
## 4 29.43
## 5 39.86
## 6 12.99