David Bickel has been an Associate Professor at the Ottawa Institute of Systems Biology since 2007. He has research interests in statistical genomics and in the foundations of statistics. After applying his statistical methods to the analysis of gene expression data, he has developed methods in response to problems with analyzing genome-wide association data and to measurements for smaller numbers of biological features (e.g., Gene Ontology terms or molecular species), especially from the metabolomics and proteomics domains. His work on the foundations of statistics focuses on hybrid methodologies, especially those based on confidence distributions, empirical Bayes approaches, and information theory.

Research programs

1 Statistical genomics

David Bickel discovers ways to assess complex information relevant to health care, renewable energy, and other applications in the post-genomic era. Improved statistical methods of weighing evidence enable more reliable interpretations of both case-control measurements of genomes and experimental measurements of transcript, protein, and metabolite levels in the cell. A more thorough understanding of these data impacts biomedicine and biotechnology, targeting higher-quality health care and sustainable energy availability.

David Bickel and the trainees in the Statomics Lab are improving statistical methods of weighing evidence to enable more reliable interpretations of both (1) experimental measurements of transcript, protein, and metabolite levels in the cell and (2) case-control measurements of genomes.

Statistical systems biology

In the first component of the research program, the lab is developing statistical methods for the analysis of gene expression microarray data and other functional genomics data. The methods include the creation and testing of new ways to estimate levels of microarray gene expression. For example, this involves work on analogous methods for the case of unpaired data such as that of proteomics and metabolomics platforms and of single-channel microarrays and reliable estimation of the fold change of each gene. Since the emerging field of lipidomics has a need for such methods of data analysis, David Bickel is a mentor in the CIHR Training Program in Neurodegenerative Lipidomics.

Inferring genome-wide associations

For the second component of this research program in high-dimensional statistics, the lab is extending similar methods developed for gene expression data to genome-wide association (GWA) studies. In particular, the lab is creating methods of reliably approximating probabilities of association in order to obtain better estimates of the effect sizes.

2 Foundations of statistics

Novel developments in statistics and information theory call for a reconsideration of important aspects of two of R. A. Fisher's most controversial ideas: the fiducial argument and the direct use of the likelihood function.

Observed confidence levels

Like the fiducial distribution, a probability measure of observed confidence levels is in effect a posterior probability distribution of the parameter of interest that does not require any prior distribution. Derived from sets of confidence intervals, this probability distribution of a parameter of interest is traditionally known as a confidence distribution. When the parameter of interest is scalar, the observed confidence level of a composite hypothesis is equal to its fiducial probability. On the other hand, observed conference levels do not suffer from the difficulties of constructing a fiducial distribution of a vector parameter.

Direct use of the likelihood function

The likelihood ratio serves not only as a tool for the construction of point estimators, p-values, confidence intervals, and posterior probabilities, but is also fruitfully interpreted as a measure of the strength of statistical evidence for one hypothesis over another through the lens of a family of distributions. Modern versions of Fisher's evidential use of the likelihood overcome multiplicity problems that arise in standard frequentism without resorting to a prior distribution.

Minimum description length principle

A related approach is to select the family of distributions using a modern information-theoretic reinterpretation of the likelihood function. In particular, the minimum description length principle extends the scope of Fisherian likelihood inference to the challenging problem of model selection.

details on statistical research

Affiliations and teaching

Before joining the Ottawa Institute of Systems Biology in 2007, David Bickel conducted research as a biostatistician at the University of Texas, the Medical College of Georgia, and Pioneer Hi-Bred International. At the University of Ottawa, he is an Associate Professor of Biochemistry, Microbiology, and Immunology and was cross-appointed to the Department of Mathematics and Statistics. His statistical research in methods of data analysis is driven by challenges posed by new measurement technologies related to genomics.

Associate Professor	University of Ottawa	Ottawa Institute of Systems Biology	Ottawa, ON	2007-present
Research Scientist—Statistics	Pioneer Hi-Bred International, Inc.	Bioinformatics	Johnston, IA	2004-2007
Assistant Professor	Medical College of Georgia	Office of Biostatistics and Bioinformatics	Augusta, GA	2001-2004
Biostatistician	University of Texas	Health Science Center	Houston, TX	1997-2001