Function implementing various statistical methods to determine the optimal number of clusters for flow cytometry data, using various clustering algorithms. ***WARNING*** extremely computationally intensive– very large data sets may not function and/or may take significant time to complete.
1 | flow_OptimalClust(flowObj, algorithm, kMax, nBoot, plot = TRUE)
|
flowObj |
A data frame or flowFrame to be analyzed |
algorithm |
The clustering algorithm to utilize. Options (no quotations): kmeans, pam (Partitioning Around Mediods), clara (Clustering LARge Applications), hcut (Hierarchical clustering, cut tree into 'k' clusters) |
kMax |
Integer representing max number of possible clusters to consider |
nBoot |
Integer representing number of times to repeat the analysis from a random starting position, must be >1 for gap statistic analysis |
plot |
Logical indicating whether or not to generate graphical plots for wss, silhouette, and gap statistic methods |
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