BayesFlow: latent modeling of flow cytometry cell populations

BMC Bioinformatics

Research Article

Kerstin Johnsson¹  Magnus Fontes²  Jonas Wallin³ 

[1] Centre for Mathematical Sciences, Lund University, Box 118, S-221 00, Lund, Sweden;Centre for Mathematical Sciences, Lund University, Box 118, S-221 00, Lund, Sweden;International Group for Data Analysis, Institut Pasteur, 25 Rue du Docteur Roux, 75015, Paris, France;Mathematical Sciences, Chalmers and University of Gothenburg, S-412 58, Gothenburg, Sweden;

关键词: Bayesian hierarchical models;    Flow cytometry;    Model-based clustering;    Primary 62P10;    Secondary 62F15;    68U99;   

DOI  :  10.1186/s12859-015-0862-z

 received in 2015-05-20, accepted in 2015-12-17,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroundFlow 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, most automated flow cytometry data analysis methods either treat samples individually or ignore the variation by for example pooling the data. A key requirement for models that include multiple samples is the ability to visualize and assess inferred variation, since what could be technical variation in one setting would be different phenotypes in another.ResultsWe introduce BayesFlow, a pipeline for latent modeling of flow cytometry cell populations built upon a Bayesian hierarchical model. The model systematizes variation in location as well as shape. Expert knowledge can be incorporated through informative priors and the results can be supervised through compact and comprehensive visualizations.BayesFlow is applied to two synthetic and two real flow cytometry data sets. For the first real data set, taken from the FlowCAP I challenge, BayesFlow does not only give a gating which would place it among the top performers in FlowCAP I for this dataset, it also gives a more consistent treatment of different samples than either manual gating or other automated gating methods. The second real data set contains replicated flow cytometry measurements of samples from healthy individuals. BayesFlow gives here cell populations with clear expression patterns and small technical intra-donor variation as compared to biological inter-donor variation.ConclusionsModeling latent relations between samples through BayesFlow enables a systematic analysis of inter-sample variation. As opposed to other joint gating methods, effort is put at ensuring that the obtained partition of the data corresponds to actual cell populations, and the result is therefore directly biologically interpretable. BayesFlow is freely available at GitHub.

【 授权许可】

CC BY   
© Johnsson et al. 2016

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