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David Bickel
Associate Professor
Department of Biochemistry, Microbiology and Immunology
Faculty of Medicine, University of Ottawa
Roger Guindon Hall, Room 4510F
451 Smyth Road, Ottawa, ON K1H 8M5
Tel: 613 562-5800 ext. 8670 (office)
Tel: 613 562-5800 ext. 8673 (Lab)
Email: d bickel [at] uottawa.ca
personal website
current lab members
The Statomics Lab currently has several exciting opportunities.
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David Bickel
Associate Professor
Degrees:
B.Sc. Baylor University 1993
M.A. University of North Texas 1994
Ph.D. University of North Texas 1997
Research overview
As a statistical scientist, 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.
Research interests
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.
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.
For the second component of this research program, the lab is extending similar methods developed for gene expression data to genome-wide association (GWA) studies, as follows. We are developing and comparing statistical methods of estimating odds ratios while considering concerns about multiple comparisons. In particular, we are inventing shrinkage estimates in the presence of multiple comparisons. We are also creating methods of reliably approximating probabilities of association in order to obtain better point and interval estimates of the effect sizes.
More on David Bickel's research
Selected Publications:
- D. R. Bickel, Z. Montazeri, P.-C. Hsieh, M. Beatty, S. J. Lawit, and N. J. Bate, "Gene network reconstruction from transcriptional dynamics under kinetic model uncertainty: A case for the second derivative," Bioinformatics 25, 772-779 (2009). Full paper | Gene network reconstruction software
- D. R. Bickel, "Probabilities of spurious connections in gene networks: Application to expression time series," Bioinformatics 21, 1121-1128 (2005).
- D. R. Bickel, "Degrees of differential gene expression: Detecting biologically significant expression differences and estimating their magnitudes," Bioinformatics 20, 682-688 (2004).
- D. R. Bickel, "Robust cluster analysis of microarray gene expression data with the number of clusters determined biologically," Bioinformatics 19,
818-824 (2003).
- D. R. Bickel, "Error-rate and decision-theoretic methods of multiple testing: Which genes have high objective probabilities of differential expression?" Statistical Applications in Genetics and Molecular Biology 3(1) 8, http://www.bepress.com/sagmb/vol3/iss1/art8 (2004).
More publications
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