Multiset Statistics for Gene Set Analysis.
An important data analysis task in statistical genomics involves the integration of genome-wide gene-level measurements with preexisting data on the same genes. A wide variety of statistical methodologies and computational tools have been developed for this general task. We emphasize one particular distinction among methodologies, namely whether they process gene sets one at a time (uniset) or simultaneously via some multiset technique. Owing to the complexity of collections of gene sets, the multiset approach offers some advantages, as it naturally accommodates set-size variations and among-set overlaps. However, this approach presents both computational and inferential challenges. After reviewing some statistical issues that arise in uniset analysis, we examine two model-based multiset methods for gene list data.
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706 ; Department of Biostatistics and Medical Informatics, University of Wisconsin, Madison, Wisconsin 53706.
Michael A
MA
Newton
Department of Statistics, University of Wisconsin, Madison, Wisconsin 53706.
Zhishi
Z
Wang
gene set enrichment; role model; statistical genomics