摘要：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 flow cytometry data distributions. Our framework
allows the automated selection of the number of distinct cell
subpopulations and we are able to identify cases where the
algorithm fails, thus making it suitable for application in a high
throughput FCM analysis pipeline. Furthermore, we demonstrate a
method for summarizing complex merged cell subpopulations in a
simple manner that integrates with the existing flowClust
framework and enables downstream data analysis. We demonstrate the
performance of our framework on simulated and real FCM data. The
software is available in the flowMerge package through the
Bioconductor project.
