Cross-species common regulatory network inference without requirement for prior gene affiliation.
BACKGROUND
Cross-species meta-analyses of microarray data usually require prior affiliation of genes based on orthology information that often relies on sequence similarity.


RESULTS
We present an algorithm merging microarray datasets on the basis of co-expression alone, without any requirement for orthology information to affiliate genes. Combining existing methods such as co-inertia analysis, back-transformation, Hungarian matching and majority voting in an iterative non-greedy hill-climbing approach, it affiliates arrays and genes at the same time, maximizing the co-structure between the datasets. To introduce the method, we demonstrate its performance on two closely and two distantly related datasets of different experimental context and produced on different platforms. Each pair stems from two different species. The resulting cross-species dynamic Bayesian gene networks improve on the networks inferred from each dataset alone by yielding more significant network motifs, as well as more of the interactions already recorded in KEGG and other databases. Also, it is shown that our algorithm converges on the optimal number of nodes for network inference. Being readily extendable to more than two datasets, it provides the opportunity to infer extensive gene regulatory networks.


METHODS
Source code (MATLAB and R) freely available for download at http://www.mchips.org/supplements/moghaddasi_source.tgz.
Gholami
Amin Moghaddas
AM
Chair of Proteomics and Bioanalytics, Center for Integrated Protein Sciences Munich (CIPSM), Technische Universität München, Emil Erlenmeyer Forum 5, 85354 Freising, Germany.
Fellenberg
Kurt
K
D000465:Algorithms; D001499:Bayes Theorem; D030541:Databases, Genetic; D020869:Gene Expression Profiling / Q000379:methods; D005786:Gene Expression Regulation; D053263:Gene Regulatory Networks; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods