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TEAM: A Multiple Testing Algorithm on the Aggregation Tree for Flow Cytometry Analysis by Pura, John; Li, Xuechan; Chan, Cliburn; Xie, Jichun is a scholarly article available to read on EtoBox.

What is TEAM: A Multiple Testing Algorithm on the Aggregation Tree for Flow Cytometry Analysis about?

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 a

Author

Pura, John; Li, Xuechan; Chan, Cliburn; Xie, Jichun

Published

2019

Language

EN