title: Simulation of cDNA microarrays via a parameterized random signal model.
abstract: cDNA microarrays provide simultaneous expression measurements for thousands of genes that are the result of processing images to recover the average signal intensity from a spot composed of pixels covering the area upon which the cDNA detector has been put down. The accuracy of the signal measurement depends on using an appropriate algorithm to process the images. This includes determining spot locations and processing the data in such a way as to take into account spot geometry, background noise, and various kinds of noise that degrade the signal. This paper presents a stochastic model for microarray images. There are over 20 model parameters, each governed by a probability distribution, that control the signal intensity, spot geometry, spot drift, background effects, and the many kinds of noise that affect microarray images owing to the manner in which they are formed. The model can be used to analyze the performance of image algorithms designed to measure the true signal intensity because the ground truth (signal intensity) for each spot is known. The levels of foreground noise, background noise, and spot distortion can be set, and algorithms can be evaluated under varying conditions.
authors.affiliation: Texas A&M University, Department of Electrical Engineering, College Station, Texas 77843-3128, USA.
authors.forename: Yoganand
authors.initials: Y
authors.lastname: Balagurunathan
authors.forename: Edward R
authors.initials: ER
authors.lastname: Dougherty
authors.forename: Yidong
authors.initials: Y
authors.lastname: Chen
authors.forename: Michael L
authors.initials: ML
authors.lastname: Bittner
authors.forename: J M
authors.initials: JM
authors.lastname: Trent
mesh_terms: D000465:Algorithms; D001699:Biometry; D007091:Image Processing, Computer-Assisted; D008962:Models, Theoretical; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D055095:Optics and Photonics; D012815:Signal Processing, Computer-Assisted; D013269:Stochastic Processes