Bayesian modelling of microarray images

Author

Ridgway, Gerard ; Godsill, Simon

Author_Institution

Dept. of Eng., Cambridge Univ., Cambridge

fYear

2006

fDate

28-30 May 2006

Firstpage

41

Lastpage

42

Abstract

We examine the use of Bayesian signal processing to improve the modelling of microarray images, and ultimately the estimation of gene expression ratios. A novel elliptical spot shape model is presented, with a Bayesian image modelling method. Prior knowledge from neighbouring spots is encompassed in the framework of a Markov random field, potentially enhancing the accuracy and reliability of ratio estimates. The techniques may be particularly beneficial for irregular, overlapping, damaged, saturated, or weakly expressed spots.

Keywords

Bayes methods; biology computing; cellular biophysics; genetics; image processing; molecular biophysics; Bayesian modelling; Bayesian signal processing; Markov random field; elliptical spot shape model; gene expression; microarray images; Bayesian methods; Gene expression; Histograms; Image analysis; Image segmentation; Markov random fields; Parametric statistics; Principal component analysis; Shape; Signal processing;

fLanguage

English

Publisher

ieee

Conference_Titel

Genomic Signal Processing and Statistics, 2006. GENSIPS '06. IEEE International Workshop on

Conference_Location

College Station, TX

Print_ISBN

1-4244-0384-7

Electronic_ISBN

1-4244-0385-5

Type

conf

DOI

10.1109/GENSIPS.2006.353146

Filename

4161767