Merging Mixture Components for Cell Population Identification in Flow Cytometry

Merging Mixture Components for Cell Population Identification in Flow Cytometry

We present a framework for the identification of cell subpopulations in flow cytometry data based on merging mixture components using the flowClust methodology. We show that the cluster merging algorithm under our framework improves model fit and provides a better estimate of the number of distinct cell subpopulations than either Gaussian mixture models or flowClust, especially for complicated ...

Merging Mixture Components for Cell Population Identification in Flow Cytometry Data The flowMerge package

Statistical mixture modeling for cell subtype identification in flow cytometry.

Statistical mixture modeling provides an opportunity for automated identification and resolution of cell subtypes in flow cytometric data. The configuration of cells as represented by multiple markers simultaneously can be modeled arbitrarily well as a mixture of Gaussian distributions in the dimension of the number of markers. Cellular subtypes may be related to one or multiple components of s...

Rapid cell population identification in flow cytometry data.

We have developed flowMeans, a time-efficient and accurate method for automated identification of cell populations in flow cytometry (FCM) data based on K-means clustering. Unlike traditional K-means, flowMeans can identify concave cell populations by modelling a single population with multiple clusters. flowMeans uses a change point detection algorithm to determine the number of sub-population...

Methods for merging Gaussian mixture components

The problem of merging Gaussian mixture components is discussed in situations where a Gaussian mixture is fitted but the mixture components are not separated enough from each other to interpret them as “clusters”. The problem of merging Gaussian mixtures is not statistically identifiable, therefore merging algorithms have to be based on subjective cluster concepts. Cluster concepts based on uni...