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TEAM: A MULTIPLE TESTING ALGORITHM ON THE AGGREGATION TREE FOR FLOW CYTOMETRY ANALYSIS
John A Pura1, Xuechan Li2, Cliburn Chan2
1Center of Innovation to Accelerate Discovery and Practice Transformation, Durham Veterans AfFaIRS MediCal Center, Durham, NC 27701.
Insights
A new method called TEAM (Testing on the Aggregation tree Method) efficiently identifies immune cells responding to stimuli in flow cytometry data. This algorithm controls false discovery rates and offers powerful insights into cellular responses.
Area of Science:
- Immunology
- Computational Biology
- Biostatistics
Background:
- Flow cytometry is a key single-cell assay in immunology for analyzing immune cell responses.
- Identifying stimulus-responsive cells involves comparing protein expression probability density functions (pdfs) before and after stimulation.
- Detecting differential pdfs is crucial for pinpointing responsive cell populations.
Purpose of the Study:
- To develop a novel, computationally efficient method for identifying differential density regions in flow cytometry data.
- To introduce TEAM (Testing on the Aggregation tree Method) for robust multiple testing and false discovery rate (FDR) control.
- To pinpoint immune cell responses to stimuli with high statistical power and biological interpretability.
Main Methods:
- Partitioning the sample space into bins to form hypotheses for differential pdfs.
- Implementing TEAM, a multiple testing method using an aggregation tree for fine-to-coarse resolution testing.
- Controlling the false discovery rate (FDR) to ensure reliable identification of differential regions.
Main Results:
- TEAM successfully identified T cells responsive to cytomegalovirus (CMV)-pp65 antigen stimulation.
- The method pinpointed enriched sets of monofunctional, bifunctional, and polyfunctional T cells.
- TEAM demonstrated computational efficiency, analyzing large datasets faster than competing methods, with valid, powerful, and robust performance.
Conclusions:
- TEAM is a statistically powerful and computationally efficient algorithm for flow cytometry data analysis.
- The method provides meaningful biological insights by accurately identifying responsive immune cell populations.
- TEAM offers a significant advancement in analyzing complex single-cell data for immunological studies.
Abstract:
In immunology studies, flow cytometry is a commonly used multivariate single-cell assay. One key goal in flow cytometry analysis is to detect 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. Further screening of these differential regions can be performed to identify enriched sets of responsive cells. In this paper, we model identifying differential density regions 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 density differences 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 cytomegalovirus (CMV)-pp65 antigen stimulation. With additional downstream screening, TEAM successfully identified enriched sets containing monofunctional, bifunctional, and polyfunctional T cells. 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.
