A Bayesian Framework for Multivariate Differential Analysis

TL;DR

This paper introduces a Bayesian framework for multivariate differential analysis, improving uncertainty quantification and handling of correlations and missing data in high-throughput data analysis.

Contribution

It presents a fully Bayesian hierarchical model for differential analysis that accounts for correlations and missing data, providing calibrated uncertainty estimates and intuitive decision criteria.

Findings

Provides a Bayesian method with closed-form equations for efficient inference.

Extends to multivariate analysis considering inter-element correlations.

Offers a new approach to interpret results using probability-based metrics like the overlap coefficient.

Abstract

Differential analysis is a routine procedure in the statistical analysis toolbox across many applied fields, including quantitative proteomics, the main illustration of the present paper. The state-of-the-art limma approach uses a hierarchical formulation with moderated-variance estimators for each analyte directly injected into the t-statistic. While standard hypothesis testing strategies are recognised for their low computational cost, allowing for quick extraction of the most differential among thousands of elements, they generally overlook key aspects such as handling missing values, inter-element correlations, and uncertainty quantification. The present paper proposes a fully Bayesian framework for differential analysis, leveraging a conjugate hierarchical formulation for both the mean and the variance. Inference is performed by computing the posterior distribution of compared experimental conditions and sampling from the distribution of differences. This approach provides well-calibrated uncertainty quantification at a similar computational cost as hypothesis testing by leveraging closed-form equations. Furthermore, a natural extension enables multivariate differential analysis that accounts for possible inter-element correlations. We also demonstrate that, in this Bayesian treatment, missing data should generally be ignored in univariate settings, and further derive a tailored approximation that handles multiple imputation for the multivariate setting. We argue that probabilistic statements in terms of effect size and associated uncertainty are better suited to practical decision-making. Therefore, we finally propose simple and intuitive inference criteria, such as the overlap coefficient, which express group similarity as a probability rather than traditional, and often misleading, p-values.