Inference on low-rank data matrices with applications to microarray data

Details Inference on low-rank data matrices with applications to microarray data Inference on low-rank data matrices with applications to microarray data

2010-09-29

arxiv.org/abs/1009.5861v1

Annals of Applied Statistics 2009, Vol. 3, No. 4, 1634-1654

Statistics

Applications

Published in at http://dx.doi.org/10.1214/09-AOAS262 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Ins

Scientific paper

10.1214/09-AOAS262

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 unidimensional 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 unidimensionality 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 unidimensional model is often indicative of practically relevant features of a probe set.

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