Statistical estimation of gene expression using multiple laser scans of microarrays.
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.


BACKGROUND
FORTRAN 90 code for implementing the method can be obtained from the authors.
Khondoker
Mizanur R
MR
Biomathematics and Statistics Scotland, University of Edinburgh King's Buildings, Edinburgh, EH9 3JZ, Scotland, UK. mizanur@bioss.ac.uk
Glasbey
Chris A
CA
Worton
Bruce J
BJ
D000465:Algorithms; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D017404:In Situ Hybridization, Fluorescence / Q000379:methods*; D018613:Microscopy, Confocal / Q000379:methods*; D008856:Microscopy, Fluorescence / Q000379:methods*; D008957:Models, Genetic; D015233:Models, Statistical; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012984:Software