aggregateData: Aggregates results from the clustering and merging step.

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

Description

Aggregates results from the clustering and merging step.

Usage

aggregateData(dataInput, labelsInput)

Arguments

dataInput	Data matrix, with first column being SampleID.
labelsInput	cluster labels from PAC.

Value

The aggregated data of dataInput, with average signal levels for all clusters and sample combinations.

Examples

n = 5e3                       # number of observations
p = 1                         # number of dimensions
K = 3                         # number of clusters
w = rep(1,K)/K                # component weights
mu <- c(0,2,4)                # component means
sd <- rep(1,K)/K              # component standard deviations
g <- sample(1:K,prob=w,size=n,replace=TRUE)   # ground truth for clustering
X <- as.matrix(rnorm(n=n,mean=mu[g],sd=sd[g]))
y <- PAC(X, K)
X2<-as.matrix(rnorm(n=n,mean=mu[g],sd=sd[g]))
y2<-PAC(X2,K)
X<-cbind("Sample1", as.data.frame(X)); colnames(X)<-c("SampleID", "Value")
X2<-cbind("Sample2", as.data.frame(X2)); colnames(X2)<-c("SampleID", "Value")
aggregateData(rbind(X,X2),c(y,y2))

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