createEnsemble: Create a cluster ensemble

In bgbrink/dropClust: Clustering algorithm for ddPCR data

Description

This function takes the three (or less) clustering approaches of the ddPCRclust package and combines them to one cluster ensemble. See cl_medoid for more information.

Usage

createEnsemble(dens = NULL, sam = NULL, peaks = NULL, file)

Arguments

dens	The result of the flowDensity algorithm as a CLUE partition.
sam	The result of the samSPECTRAL algorithm as a CLUE partition.
peaks	The result of the flowPeaks algorithm as a CLUE partition.
file	The input data. More specifically, a data frame with two dimensions, each dimension representing the intensity for one color.

Value

data	The original input data minus the removed events (for plotting)
confidence	The agreement between the different clustering results in percent. If all algorithms calculated the same result, the clustering is likely to be correct, thus the confidence is high.
counts	The droplet count for each cluster.

Examples

exampleFiles <- list.files(paste0(find.package('ddPCRclust'), '/extdata'), full.names = TRUE)
file <- read.csv(exampleFiles[3])
densResult <- runDensity(file = file, numOfMarkers = 4)
samResult <- runSam(file = file, numOfMarkers = 4)
peaksResult <- runPeaks(file = file, numOfMarkers = 4)

superResult <- createEnsemble(densResult, samResult, peaksResult, file)

bgbrink/dropClust documentation built on Nov. 7, 2019, 12:18 p.m.