Genomic studies have become commonplace, with thousands of gene expressions typically collected on single or multiple platforms and analyzed. Unaccounted time-ordered or epigenetic aspects of genetic expression may lead to a version of Simpson's paradox, ie, time-aggregated overall effects that do not reflect within strata patterns. Without clear functional models to motivate clustering and fitting algorithms, these confounding related issues require consideration. Several basic examples motivate discussion and more appropriate models for analysis of expression data are reviewed.
A grant from the One-University Open Access Fund at the University of Kansas was used to defray the author’s publication fees in this Open Access journal. The Open Access Fund, administered by librarians from the KU, KU Law, and KUMC libraries, is made possible by contributions from the offices of KU Provost, KU Vice Chancellor for Research & Graduate Studies, and KUMC Vice Chancellor for Research. For more information about the Open Access Fund, please see http://library.kumc.edu/authors-fund.xml.
Brimacombe, M. (2014). Genomic aggregation effects and Simpson’s paradox. http://dx.doi.org/10.2147/OAMS.S52288