Conceptual Aspects of Large Meta-Analyses with Publicly Available Microarray Data: A case study in Oncology

Large public repositories of microarray experiments offer an abundance of biological data. It is of interest to use and to combine the available material to create new biological information and to develop a broader view on biological phenomena. Meta-analyses recombine similar information over a series of experiments to sketch scientific aspects which were not accessible by each of the single experiments. Meta-analysis of high-throughput experiments has to handle methodological as well as technical challenges. Methodological aspects concern the identification of homogeneous material which can be combined by appropriate statistical procedures. Technical challenges come from the data management of large amounts of high-dimensional data, long computation time, as well as the handling of the stored phenotype data. This paper compares in a meta-analysis of a large series of microarray experiments the interaction structure within selected pathways between different tumour entities. The feasibility of such a study is explored and a technical as well as a statistical framework for its completion is presented. Multiple obstacles were met during completion of this project. They are mainly related to the quality of the available data and influence the biological interpretation of the results derived. The sobering experience of our study asks for combined efforts to improve the data quality in public repositories of high-throughput data. The exploration of the available data in large meta-analyses is limited by incomplete documentation of essential aspects of experiments and studies, by technical deficiencies in the data stored, and by careless duplications of data.

Abstract licence: CC BY-NC

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.