Flow cytometry is a widespread single-cell measurement technology with a multitude of clinical and research applications. Interpretation of flow cytometry data is hard; the instrumentation is delicate and can not render absolute measurements, hence samples can only be interpreted in relation to each other while at the same time comparisons are confounded by inter-sample variation. Despite this, current automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. In this article we introduce a Bayesian hierarchical model for studying latent relations between cell populations in flow cytometry samples, thereby systematizing inter-sample variation. The model is applied to a data set containing replicated flow cytometry measurements of samples from healthy individuals, with informative priors capturing expert knowledge. It is shown that the technical variation in the inferred cell population sizes is small in comparison to the intrinsic biological variation. The large size of flow cytometry data, where a single sample can contain measurements on hundreds of thousands of cells, necessitates computationally efficient methods. To address this, we have implemented a parallel Markov Chain Monte Carlo scheme for sampling the posterior distribution.
Comparison of statistical methods for high-dimensional compositional data from flow cytometry: A critical perspective on log-ratio transformation and differential abundance testing
National Cancer Institute/National Institutes of Health（美国国立癌症研究所/美国国立卫生研究院）
A probabilistic latent variable model for detecting structure in binary data
University of California Berkeley（加州大学伯克利分校）；Duke University School of Medicine（杜克大学医学院）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Harvard Medical School（哈佛医学院）；Intel Labs（英特尔实验室）
Inherited or produced? Inferring protein production kinetics when protein counts are shaped by a cell's division history
Center for Biological Physics, Arizona State University（亚利桑那州立大学生物物理中心）；Department of Physics, Arizona State University（亚利桑那州立大学物理系）；School of Molecular Sciences, Arizona State University（亚利桑那州立大学分子科学学院）
Physics-informed distribution of relaxation times estimation and latent-space condition monitoring of solid oxide fuel and electrolysis cells from electrochemical impedance spectroscopy
Jožef Stefan Institute（约热·斯特凡研究所）；Jožef Stefan International Postgraduate School（约热·斯特凡国际研究生学院）；Graz University of Technology（格拉茨工业大学）；Faculty of Information Studies in Novo mesto（新梅斯托信息研究学院）
