A Bayesian model for microarray datasets merging

TL;DR

This paper introduces a Bayesian model for merging microarray datasets by explicitly modeling interstudy variability, enabling more accurate data aggregation and biological effect detection across studies.

Contribution

It presents a novel Bayesian algorithm that models study-dependent nonlinear transformations and noise, improving dataset merging and biological signal recovery.

Findings

Successfully corrected nonlinear distortions in E. Coli data

Enhanced detection of differentially expressed genes in prostate cancer datasets

Validated method improves data integration accuracy

Abstract

The aggregation of microarray datasets originating from different studies is still a difficult open problem. Currently, best results are generally obtained by the so-called meta-analysis approach, which aggregates results from individual datasets, instead of analyzing aggre-gated datasets. In order to tackle such aggregation problems, it is necessary to correct for interstudy variability prior to aggregation. The goal of this paper is to present a new approach for microarray datasets merging, based upon explicit modeling of interstudy variability and gene variability. We develop and demonstrate a new algorithm for microarray datasets merging. The underlying model assumes normally distributed intrinsic gene expressions, distorted by a study-dependent nonlinear transformation, and study dependent (normally distributed) observation noise. The algorithm addresses both parameter estimation (the parameters being gene expression means and variances, observation noise variances and the nonlinear transformations) and data adjustment, and yields as a result adjusted datasets suitable for aggregation. The method is validated on two case studies. The first one concerns E. Coli expression data, artificially distorted by given nonlinear transformations and additive observation noise. The proposed method is able to correct for the distortion, and yields adjusted datasets from which the relevant biological effects can be recovered, as shown by a standard differential analysis. The second case study concerns the aggregation of two real prostate cancer datasets. After adjustment using the proposed algorithm, a differential analysis performed on adjusted datasets yields a larger number of differentially expressed genes (between control and tumor data). The proposed method has been implemented using the statistical software R 1, and Bioconductor packages 2. The source code (valid for merging two datasets), as well as the datasets used for the validation, and some complementary results, are made available on the web site