title: WaveRead: automatic measurement of relative gene expression levels from microarrays using wavelet analysis.
abstract: Gene expression microarrays monitor the expression levels of thousands of genes in an experiment simultaneously. To utilize the information generated, each of the thousands of spots on a microarray image must be properly quantified, including background correction. Most present methods require manual alignment of grids to the image data, and still often require additional minor adjustments on a spot by spot basis to correct for spotting irregularities. Such intervention is time consuming and also introduces inconsistency in the handling of data. A fully automatic, tested system would increase throughput and reliability in this field. In this paper, we describe WaveRead, a fully automated, standalone, open-source system for quantifying gene expression array images. Through the use of wavelet analysis to identify the spot locations and diameters, the system is able to automatically grid the image and quantify signal intensities and background corrections without any user intervention. The ability of WaveRead to perform proper quantification is demonstrated by analysis of both simulated images containing spots with donut shapes, elliptical shapes, and Gaussian intensity distributions, as well as of standard images from the National Cancer Institute.
authors.affiliation: Bioinformatics, Division of Population Science, Fox Chase Cancer Center, 333 Cottman Avenue, Philadelphia, PA 19111-2497, USA.
authors.forename: Ghislain
authors.initials: G
authors.lastname: Bidaut
authors.forename: Frank J
authors.initials: FJ
authors.lastname: Manion
authors.forename: Christophe
authors.initials: C
authors.lastname: Garcia
authors.forename: Michael F
authors.initials: MF
authors.lastname: Ochs
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D015870:Gene Expression / Q000502:physiology*; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012984:Software