Increasing statistical power and generalizability in genomics microarray research

The high-throughput technologies developed in the last decade have revolutionized the speed of data accumulation in the life sciences. As a result we have very rich and complex data that holds great promise to solving many complex biological questions. One such technology that is very well established and widespread is DNA microarrays, which allows one to simultaneously measure the expression levels of tens of thousands of genes in a biological tissue. This thesis aims to contribute to the development of statistics that allow the end users to obtain robust and meaningful results from DNA microarrays for further investigation. The methodology, implementation and pragmatic issues of two important and related topics – sample size estimations for designing new studies and meta-analysis of existing studies – are presented here to achieve this aim. Real life case studies and guided steps are also given. Sample size estimation is important at the design stage to ensure a study has sufficient statistical power to address the stated objective given the financial constraints. The commonly used formula for estimating the number of biological samples, its short-comings and potential amelioration are discussed. The optimal number of biological samples and number of measurements per sample that minimizes the cost is also presented. Meta-analysis or the synthesis of information from existing studies is very attractive because it can increase the statistical power by making comprehensive and inexpensive use of available information. Furthermore, one can also easily test the generalizability of findings (i.e. the extent of results from a particular valid study can be applied to other circumstances). The key issues in conducting a meta-analysis for microarrays studies, a checklist and R codes are presented here. Finally, the poor availability of raw data in microarray studies is discussed here with recommendations for authors, journal editors and funding bodies. Good availability of data is important for meta-analysis in order to avoid biased results and for sample size estimation