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Pre-processing of quantitative phenotypes from high throughput studies

University of British Columbia

High throughput phenotypic experiments include both deletion sets and R N A i experiments. They are genome wide and require much physical space. As a result, multiple plates are often required in order to cover the whole genome. The use of multiple plates leads to systematic plate-wise experimental artefact, which impede statistical inference. In this paper, current pre-processing methodology will be reviewed. Their fundamental principle is to align a common feature shared by all plates. From this very principle, we propose an improved method which simultaneously estimates all parameters required for the pre-processing transformation. Some of the alignment features popular today implicitly assume conditions which are often not met in practice. We discuss the various choices of features to align. Specifically, the upper quantiles and the mean of the left tail trimmings of each plate's data distribution are features which are always available and simple to obtain. Moreover, they are robust to non-randomization of genes to plates. Their use will be motivated through simulation and applied to real data. Applications to real data will be used to demonstrate superiority over current methods as well as to discuss choices in transformation types.

Text

2011-02-17

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http://hdl.handle.net/2429/31404

Statistics

University of British Columbia

Graduate