Cytometry is an important single cell analysis technology in furthering our understanding of cellular biological processes and in supporting clinical diagnoses across a variety hematological and immunological conditions. Current data analysis workflows for cytometry data rely on a manual process called gating to classify cells into canonical types. This dependence on human annotation significantly limits the rate, reproducibility, and scope of cytometry's use in both biological research and clinical practice. We develop a novel Bayesian approach for automated gating that classifies cells into different types by combining cell-level marker measurements with an informative prior. The Bayesian approach allows for the incorporation of biologically-meaningful prior information that captures the domain expertise of human experts. The inference algorithm results in a hierarchically-structured classification of individual cells in a manner that mimics the tree-structured recursive process of manual gating, making the results readily interpretable. The approach can be extended in a natural fashion to handle data from multiple different samples by the incorporation of random effects in the Bayesian model. The proposed approach is evaluated using mass cytometry data, on the problems of unsupervised cell classification and supervised clinical diagnosis, illustrating the benefits of both incorporating prior knowledge and sharing information across multiple samples.

Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a crucial need to assess the performance of these pre-trained models, for instance to ensure sufficient predictive accuracy, or that class probabilities are well-calibrated. Furthermore, since labeled data may be scarce or costly to collect, it is desirable for such assessment be performed in an efficient manner. In this paper, we introduce a Bayesian approach for model assessment that satisfies these desiderata. We develop inference strategies to quantify uncertainty for common assessment metrics (accuracy, misclassification cost, expected calibration error), and propose a framework for active assessment using this uncertainty to guide efficient selection of instances for labeling. We illustrate the benefits of our approach in experiments assessing the performance of modern neural classifiers (e.g., ResNet and BERT) on several standard image and text classification datasets.

High‐throughput single‐cell cytometry technologies have significantly improved our understanding of cellular phenotypes to support translational research and the clinical diagnosis of hematological and immunological diseases. However, subjective and ad hoc manual gating analysis does not adequately handle the increasing volume and heterogeneity of cytometry data for optimal diagnosis. Prior work has shown that machine learning can be applied to classify cytometry samples effectively. However, many of the machine learning classification results are either difficult to interpret without using characteristics of cell populations to make the classification, or suboptimal due to the use of inaccurate cell population characteristics derived from gating boundaries. To date, little has been done to optimize both the gating boundaries and the diagnostic accuracy simultaneously. In this work, we describe a fully discriminative machine learning approach that can simultaneously learn feature representations (e.g., combinations of coordinates of gating boundaries) and classifier parameters for optimizing clinical diagnosis from cytometry measurements. The approach starts from an initial gating position and then refines the position of the gating boundaries by gradient descent until a set of globally‐optimized gates across different samples are achieved. The learning procedure is constrained by regularization terms encoding domain knowledge that encourage the algorithm to seek interpretable results. We evaluate the proposed approach using both simulated and real data, producing classification results on par with those generated via human expertise, in terms of both the positions of the gating boundaries and the diagnostic accuracy. © 2019 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.

Recent advances in machine learning have led to increased deployment of black-box classifiers across a wide variety of applications. In many such situations there is a critical need to both reliably assess the performance of these pre-trained models and to perform this assessment in a label-efficient manner (given that labels may be scarce and costly to collect). In this paper, we introduce an active Bayesian approach for assessment of classifier performance to satisfy the desiderata of both reliability and label-efficiency. We begin by developing inference strategies to quantify uncertainty for common assessment metrics such as accuracy, misclassification cost, and calibration error. We then propose a general framework for active Bayesian assessment using inferred uncertainty to guide efficient selection of instances for labeling, enabling better performance assessment with fewer labels. We demonstrate significant gains from our proposed active Bayesian approach via a series of systematic empirical experiments assessing the performance of modern neural classifiers (e.g., ResNet and BERT) on several standard image and text classification datasets.

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