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W2076901504 abstract "Probe-level microarray data are usually stored in matrices, where the row and column correspond to array and probe, respectively. Scientists routinely summarize each array by a single index as the expression level of each probe-set (gene). We examine the adequacy of a uni-dimensional summary for characterizing the data matrix of each probe-set. To do so, we propose a low-rank matrix model for the probe-level intensities, and develop a useful framework for testing the adequacy of uni-dimensionality against targeted alternatives. This is an interesting statistical problem where inference has to be made based on one data matrix whose entries are not i.i.d. We analyze the asymptotic properties of the proposed test statistics, and use Monte Carlo simulations to assess their small sample performance. Applications of the proposed tests to GeneChip data show that evidence against a uni-dimensional model is often indicative of practically relevant features of a probe-set." @default.
W2076901504 created "2016-06-24" @default.
W2076901504 creator A5043907454 @default.
W2076901504 creator A5047591308 @default.
W2076901504 date "2009-12-01" @default.
W2076901504 modified "2023-09-30" @default.
W2076901504 title "Inference on low-rank data matrices with applications to microarray data" @default.
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W2076901504 doi "https://doi.org/10.1214/09-aoas262" @default.
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