  In immunology studies, flow cytometry is a commonly used multivariate
single-cell assay. One key goal in flow cytometry analysis is to pinpoint the
immune cells responsive to certain stimuli. Statistically, this problem can be
translated into comparing two protein expression probability density functions
(PDFs) before and after the stimulus; the goal is to pinpoint the regions where
these two pdfs differ. In this paper, we model this comparison as a multiple
testing problem. First, we partition the sample space into small bins. In each
bin we form a hypothesis to test the existence of differential pdfs. Second, we
develop a novel multiple testing method, called TEAM (Testing on the
Aggregation tree Method), to identify those bins that harbor differential pdfs
while controlling the false discovery rate (FDR) under the desired level. TEAM
embeds the testing procedure into an aggregation tree to test from fine- to
coarse-resolution. The procedure achieves the statistical goal of pinpointing
differential pdfs to the smallest possible regions. TEAM is computationally
efficient, capable of analyzing large flow cytometry data sets in much shorter
time compared with competing methods. We applied TEAM and competing methods on
a flow cytometry data set to identify T cells responsive to the cytomeglovirus
(CMV)-pp65 antigen stimulation. TEAM successfully identified the
monofunctional, bifunctional, and polyfunctional T cells while the competing
methods either did not finish in a reasonable time frame or provided less
interpretable results. Numerical simulations and theoretical justifications
demonstrate that TEAM has asymptotically valid, powerful, and robust
performance. Overall, TEAM is a computationally efficient and statistically
powerful algorithm that can yield meaningful biological insights in flow
cytometry studies.
