A Robust Algorithm for Ratio Estimation in Two-color Microarray Experiments

The reliability of the algorithms for ratio estimation in two-color microarray image analysis is very important, as these ratios build up the primary source of information for the subsequent analytical procedures (normalization, clustering, classification, etc). Although various algorithms already exist, there is still a need to develop procedures having higher levels of accuracy and robustness. We present a statistical procedure for the detection and removal of aberrant pixels in two-color microarray images. It is based on a linear regression approach, assuming reasonably high level of correlation between the two color channels. This procedure ensures more robust ratio estimation for the spots in both linear regression and traditional segmentation algorithms. The developed algorithms have been evaluated using simulated artificial images and experimental images of different designs. A demonstration version of the software can be downloaded from http://bioinfo.curie.fr/projects/maia/.

[1]  A. Poustka,et al.  Parameter estimation for the calibration and variance stabilization of microarray data , 2003, Statistical applications in genetics and molecular biology.

[2]  Anthony C. Atkinson,et al.  Robust Diagnostic Regression Analysis , 2000 .

[3]  N. Lee,et al.  A concise guide to cDNA microarray analysis. , 2000, BioTechniques.

[4]  Terence P. Speed,et al.  Comparison of Methods for Image Analysis on cDNA Microarray Data , 2002 .

[5]  Ajay N. Jain,et al.  Fully automatic quantification of microarray image data. , 2002, Genome research.

[6]  W. Kuo,et al.  High resolution analysis of DNA copy number variation using comparative genomic hybridization to microarrays , 1998, Nature Genetics.

[7]  Jesús Angulo,et al.  Automatic analysis of DNA microarray images using mathematical morphology , 2003, Bioinform..

[8]  Jörg Rahnenführer,et al.  Unsupervised technique for robust target separation and analysis of DNA microarray spots through adaptive pixel clustering , 2002, Bioinform..

[9]  Ralf Herwig,et al.  Simulation of DNA array hybridization experiments and evaluation of critical parameters during subsequent image and data analysis , 2002, BMC Bioinformatics.

[10]  Peter J. Rousseeuw,et al.  Robust regression and outlier detection , 1987 .

[11]  Petr Kuzmic,et al.  Practical Robust Fit of Enzyme Inhibition Data , 2004, Numerical Computer Methods, Part D.

[12]  Chris A. Glasbey,et al.  Combinatorial image analysis of DNA microarray features , 2003, Bioinform..