Statistical estimation of gene expression using multiple laser scans of microarrays

M R  Khondoker; C A  Glasbey; B J  Worton

doi:10.1093/bioinformatics/bti790

Statistical estimation of gene expression using multiple laser scans of microarrays

M R Khondoker, C A Glasbey, B J Worton

Biostatistics & Health Informatics

Biomathematics and Statistics Scotland (BioSS)

Research output: Contribution to journal › Article › peer-review

25 Citations (Scopus)

Abstract

We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias.

Original language	English
Pages (from-to)	215-219
Number of pages	5
Journal	BIOINFORMATICS
Volume	22
Issue number	2
DOIs	https://doi.org/10.1093/bioinformatics/bti790
Publication status	Published - 15 Jan 2006

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abstract = "We propose a statistical model for estimating gene expression using data from multiple laser scans at different settings of hybridized microarrays. A functional regression model is used, based on a non-linear relationship with both additive and multiplicative error terms. The function is derived as the expected value of a pixel, given that values are censored at 65 535, the maximum detectable intensity for double precision scanning software. Maximum likelihood estimation based on a Cauchy distribution is used to fit the model, which is able to estimate gene expressions taking account of outliers and the systematic bias caused by signal censoring of highly expressed genes. We have applied the method to experimental data. Simulation studies suggest that the model can estimate the true gene expression with negligible bias.",

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Statistical estimation of gene expression using multiple laser scans of microarrays

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