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Research at the Statomics Lab


At the Statomics Lab, we discover 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, 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 information

For details on the research summarized above, see the lab’s publications and preprints.

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