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Paper Detail

Paper:	BIO-L2.2
Session:	Bioinformatics and Genomics
Time:	Friday, May 19, 10:20 - 10:40
Presentation:	Lecture
Topic:	Bio Imaging and Signal Processing: Bioinformatics, genomics, and proteomics
Title:	Normalization of cDNA Microarray Data based on Least Absolute Deviation Regression
Authors:	Juan Ramirez, Jose Paredes, Universidad de Los Andes, Venezuela; Gonzalo Arce, University of Delaware, United States
Abstract:	This paper proposes a method of normalization of cDNA microarray data. This approach uses all gene data to estimate the normalization parameters and it obtains these parameters using an algorithm based on Least Absolute Deviation (LAD) regression. This method normalizes iteratively each microarray set after the estimation of the normalization parameters which uses a LAD regression algorithm. The normalization method has a robust performance since it assumes that the errors between arrays follow a Laplacian distribution, leading to the mean absolute error minimization as a performance criterion to be achieved. The proposed normalization method was evaluated using three performance measures and they show that LAD based normalization method minimizes the errors and provides a more consistent replicated data spread with respect to a least square based method.