A conditional predictive p-value to compare a multinomial with an overdispersed multinomial in the analysis of T-cell populations

• Published in: Biostatistics (Oxford, England) Vol. 15; no. 1; pp. 129 - 139
• Main Authors: Pei, Qinglin, Zuleger, Cindy L, Macklin, Michael D, Albertini, Mark R, Newton, Michael A
• Format: Journal Article
• Language: English
• Published: England Oxford Publishing Limited (England) 01.01.2014; Oxford University Press
• Subjects: Bayesian analysis; Biostatistics; Cell Proliferation; Complementarity Determining Regions - genetics; Complementarity Determining Regions - immunology; Data Interpretation, Statistical; Humans; Immunology; Models, Statistical; Mutation - genetics; Mutation - immunology; Receptors, Antigen, T-Cell - immunology; Sequence Analysis, DNA; T cell receptors; T-Lymphocytes - immunology; T-cell receptor; Dirichlet multinomial; Bayesian p-value; Mass culture experiments; Molecular sequence data; Double overdispersion; Fisher's exact test; HPRT assay
• ISSN: 1465-4644; 1468-4357
• DOI: 10.1093/biostatistics/kxt039

Immunological experiments that record primary molecular sequences of T-cell receptors produce moderate to high-dimensional categorical data, some of which may be subject to extra-multinomial variation caused by technical constraints of cell-based assays. Motivated by such experiments in melanoma research, we develop a statistical procedure for testing the equality of two discrete populations, where one population delivers multinomial data and the other is subject to a specific form of overdispersion. The procedure computes a conditional-predictive p-value by splitting the data set into two, obtaining a predictive distribution for one piece given the other, and using the observed predictive ordinate to generate a p-value. The procedure has a simple interpretation, requires fewer modeling assumptions than would be required of a fully Bayesian analysis, and has reasonable operating characteristics as evidenced empirically and by asymptotic analysis.