Robust segmentation and analysis of DNA microarray spots using an adaptative split and merge algorithm.
Microarray images push to their limits classical analysis methods, since gene spots are often poorly contrasted, ill defined and of irregular shapes. These characteristics hinder a robust quantification of corresponding values for red and green intensities as well as their R/G ratio. New approaches are thus needed to ensure accurate data extraction from these images. Herein we present an automatic non-supervised algorithm for a fast and accurate spot data extraction from DNA microarrays. The method is based on a split and merge algorithm, relying on a Delaunay triangulation process, allowing an incremental partition of the image into homogeneous polygons. Geometric properties of triangles as well as homogeneity criteria are defined according to the specificities of microarray image signals. The method is first assessed on simulated data, and then compared with GenePix and Jaguar Softwares. Results in segmentation and quantification are superior to those obtained from a number of standard techniques for spot extraction.
Barra
Vincent
V
LIMOS, UMR 6158 CNRS, Campus des Cézeaux, 63117 Aubiere, France. vincent.barra@isima.fr
D000465:Algorithms; D003627:Data Interpretation, Statistical; D005602:France; D020869:Gene Expression Profiling; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*