Competitive SWIFT cluster templates enhance detection of aging changes.Clustering-based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing.  Here, we address this problem by augmenting SWIFT--a mixture model based clustering algorithm reported previously.  First, we show that SWIFT clustering using a "template" mixture model, in which all subpopulations are represented, identifies small differences in cell numbers per subpopulation between samples.  Several changes between the young and elderly identified by SWIFT template competition were consistent with known alterations in the elderly, and additional altered subpopulations were also identified.  Thus SWIFT template competition is a powerful approach to sharpen comparisons between selected groups in flow cytometry datasets.
SWIFT
Competitive SWIFT cluster templates enhance detection of aging changes.Clustering-based algorithms for automated analysis of flow cytometry datasets have achieved more efficient and objective analysis than manual processing. 
SWIFT cluster templates