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General Departmental Seminar Series

Testing differential expression in non-overlapping gene pairs:
A new perspective for the empirical Bayes method

Andrei Yakovlev
Rochester University

Wednesday, December 5
4:00 pm
140 Bardeen

Joint Seminar



The currently practiced methods of significance testing in microarray gene expression profiling are highly unstable and their power tends to be very low. These undesirable properties are due to the nature of multiple testing procedures, as well as extremely strong and long-ranged correlations between gene expression levels. Resorting to normalization procedures does not provide a satisfactory solution to the problem because of their distorting effects on the true expression signals. Such effects are especially pronounced in large sample studies where control of type 1 errors may be entirely lost. We have identified a special structure in gene expression data that produces a sequence of weakly dependent random variables. This structure, termed the delta-sequence, lies at the heart of a new methodology for selecting differentially expressed genes in non-overlapping gene pairs. The proposed method has two distinct advantages: (1) it leads to dramatic gains in terms of the mean numbers of true and false discoveries, as well as in stability of the results of testing; (2) its outcomes are entirely free from the log-additive array-specific technical noise.  We demonstrate the usefulness of this approach in conjunction with the nonparametric empirical Bayes methodology.


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