Plant Systems Biology

Volume 553 of the series Methods in Molecular Biology™ pp 181-206


Challenges and Approaches to Statistical Design and Inference in High-Dimensional Investigations

  • Gary L. GadburyAffiliated withDepartment of Statistics, Kansas State University
  • , Karen A. GarrettAffiliated withDepartment of Plant Pathology, Kansas State University
  • , David B. AllisonAffiliated withSection on Statistical Genetics, Department of Biostatistics, University of Alabama at Birmingham

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Advances in modern technologies have facilitated high-dimensional experiments (HDEs) that generate tremendous amounts of genomic, proteomic, and other “omic” data. HDEs involving whole-genome sequences and polymorphisms, expression levels of genes, protein abundance measurements, and combinations thereof have become a vanguard for new analytic approaches to the analysis of HDE data. Such situations demand creative approaches to the processes of statistical inference, estimation, prediction, classification, and study design. The novel and challenging biological questions asked from HDE data have resulted in many specialized analytic techniques being developed. This chapter discusses some of the unique statistical challenges facing investigators studying high-dimensional biology and describes some approaches being developed by statistical scientists. We have included some focus on the increasing interest in questions involving testing multiple propositions simultaneously, appropriate inferential indicators for the types of questions biologists are interested in, and the need for replication of results across independent studies, investigators, and settings. A key consideration inherent throughout is the challenge in providing methods that a statistician judges to be sound and a biologist finds informative.

Key words

FDR genomics high-dimensional microarray multiple testing statistics