Epitope profiling via mixture modeling of ranked data

• Published in: Statistics in medicine Vol. 33; no. 21; pp. 3738 - 3758
• Main Authors: Mollica, Cristina, Tardella, Luca
• Format: Journal Article
• Language: English
• Published: England Blackwell Publishing Ltd 20.09.2014; Wiley Subscription Services, Inc
• Subjects: Algorithms; Bioassays; Cluster Analysis; Computer Simulation; Data Interpretation, Statistical; EM algorithm; epitope mapping; Estimating techniques; Humans; Likelihood Functions; Maximum likelihood method; Medical statistics; mixture models; Models, Statistical; multistage ranking models; Plackett-Luce model; Probability distribution; ranking data; mixture models; EM algorithm; Plackett-Luce model; epitope mapping; multistage ranking models; ranking data
• ISSN: 0277-6715; 1097-0258; 1097-0258
• DOI: 10.1002/sim.6224

We propose the use of probability models for ranked data as a useful alternative to a quantitative data analysis to investigate the outcome of bioassay experiments when the preliminary choice of an appropriate normalization method for the raw numerical responses is difficult or subject to criticism. We review standard distance‐based and multistage ranking models and propose an original generalization of the Plackett–Luce model to account for the order of the ranking elicitation process. The usefulness of the novel model is illustrated with its maximum likelihood estimation for a real data set. Specifically, we address the heterogeneous nature of the experimental units via model‐based clustering and detail the necessary steps for a successful likelihood maximization through a hybrid version of the expectation–maximization algorithm. The performance of the mixture model using the new distribution as mixture components is then compared with alternative mixture models for random rankings. A discussion on the interpretation of the identified clusters and a comparison with more standard quantitative approaches are finally provided. Copyright © 2014 John Wiley & Sons, Ltd.