Template-based semi-automatic workflow construction for gene expression data analysis

We propose a technique for semi-automatic construction of gene expression data analysis workflows by grammar-like inference based on predefined workflow templates. The templates represent routinely used sequences of procedures such as normalization, data transformation, classifier learning, etc. Variations of such workflows (such as different instantiations to specific algorithms) may entail significant variance in the quality of the analysis results and our formalism enables to automatically explore such variations. Adhering to proven templates helps preserve the sanity of explored workflows and prevents the combinatorial explosion encountered by fully automatic workflow planners. Here we propose the basic principles of template-based workflow construction and demonstrate their working in the publicly available tool XGENE.ORG for multi-platform gene expression analysis.