Efficiency of functional regression estimators for combining multiple laser scans of cDNA microarrays

Glasbey, C. A. and Khondoker, M. R. ORCID: https://orcid.org/0000-0002-1801-1635 (2009) Efficiency of functional regression estimators for combining multiple laser scans of cDNA microarrays. Biometrical Journal, 51 (1). pp. 45-55. ISSN 0323-3847

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Abstract

The first stage in the analysis of cDNA microarray data is estimation of the level of expression of each gene, from laser scans of hybridised microarrays. Typically, data are used from a single scan, although, if multiple scans are available, there is the opportunity to reduce sampling error by using all of them. Combining multiple laser scans can be formulated as multivariate functional regression through the origin. Maximum likelihood estimation fails, but many alternative estimators exist, one of which is to maximise the likelihood of a Gaussian structural regression model. We show by simulation that, surprisingly, this estimator is efficient for our problem, even though the distribution of gene expression values is far from Gaussian. Further, it performs well if errors have a heavier tailed distribution or the model includes intercept terms, but not necessarily in other regions of parameter space. Finally, we show that by combining multiple laser scans we increase the power to detect differential expression of genes. (© 2009 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Item Type:	Article
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health Faculty of Medicine and Health Sciences > Research Groups > Public Health and Health Services Research (former - to 2023) Faculty of Science > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre Faculty of Medicine and Health Sciences > Research Centres > Population Health
Depositing User:	Pure Connector
Date Deposited:	24 Sep 2016 00:41
Last Modified:	19 Oct 2023 01:46
URI:	https://ueaeprints.uea.ac.uk/id/eprint/60180
DOI:	10.1002/bimj.200710444

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