## INTRODUCTION

Multiparameter flow cytometry (FCM) technology has seen dramatic advances in recent years, with five or more color assays now performed routinely in many basic and translational research laboratories (1). Standardization of all aspects of FCM, from instrument setup to data analysis, is an ongoing effort by multiple organizations, because standardization is necessary for consistent data comparison across sites (2). Multiple investigators have pioneered the use of advanced techniques and technologies to accurately evaluate immune cell subsets in multicenter programs for well over two decades, including the use of backgating for measuring purity and recovery combined with checksums (3), use of CD45 in three-color (4) and four-color assays (5), use of a single platform technology for absolute counts (6, 7), panleukogating (8), and the use of prealiquoted lyophilized reagents (2).

In the context of FCM assays performed in good clinical laboratory practice (GCLP)-compliant laboratories, demonstration of reproducibility is critical for clinical acceptance (9–11). The reproducibility of FCM assays relies on key elements of the assay being standardized and well-characterized, including instrument and reagent qualification, sample preparation processes, and analysis protocols (12–15). Over the past few years, multicenter standardization studies for many types of flow assays have consistently shown that suboptimal data analysis methods are one of the most significant sources of variability (2, 16, 17). Variability can be reduced by collection of sufficient events, use of appropriate controls, careful parameter selection, and optimized gating strategies (18–21). In the context of analysis, the use of highest purity and lowest contamination measures as well as backgating can aid in the design of appropriate gates.

Simple gates in any two projected dimensions may not suffice to minimize false positive and maximize true positive events. Hence, in the drive to maximize recovery and purity (22), gating strategies can become increasingly complex even when there are relatively few parameters being measured, with arbitrarily shaped gates and multiple gating generations being used. Although this is effective for a single laboratory, it is difficult to apply complex gating strategies consistently across different instruments and operators across multiple laboratories. Thus, the ability to objectively measure the contribution of a specific parameter or combination of parameters toward target cell identification independent of any gating strategy could be very helpful for both panel and gating strategy design.

Recent developments in computational statistics allow us to discover and monitor target cell subsets directly in multiple dimensions without use of a sequence of gates. Several groups, including ours, have recently published gating-free model-based approaches to cell subset identification using statistical mixtures of Gaussian, T, or skewed distributions (23–26). Here, we show that the predictive density resulting from such model-based approaches can be exploited to perform a discriminative information measure evaluation (DIME) for FCM parameters. DIME analysis allows us evaluate parameter usefulness for identifying a target cell subset that can be specified as some collection of mixture components. From a biological perspective, DIME provides insight into optimal parameter combinations that characterize a cell subset in a way that is independent of any particular gating strategy. Practically, DIME provides an objective basis for standardizing the analysis of FCM panels in multicenter clinical trials and can contribute to improved assay reproducibility.

We show the application of DIME to the design of a simplified gating strategy for a carboxyfluorescein succinimidyl ester (CFSE)-based assay designed to measure CD4 and CD8 T lymphocyte proliferation following antigen challenge. The context for this proof-of-concept analysis was a three center pilot study (BD Biosciences, Université de Montreal/NIML and Duke University) sponsored by DAIDS to standardize the assessment of T lymphocyte proliferation using a panel for CD3, CD4, CD8, CFSE, and an amine viability stain. Experts at the three centers had, through careful evaluation of their collective data, developed a standard consensus gating strategy that was designed to reduce background and enhance detection of specific proliferation.