  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
