H P Piepho1, B Keller, N Hoecker, F Hochholdinger. 1. Bioinformatics Unit, Institute for Crop Production and Grassland Research, University of Hohenheim, Fruwirthstrasse 23, 70599 Stuttgart, Germany. piepho@uni-hohenheim.de
Abstract
MOTIVATION: The analysis of spotted cDNA microarrays involves scanning of color signals from fluorescent dyes. A common problem is that a given scanning intensity is not usually optimal for all spotted cDNAs. Specifically, some spots may be at the saturation limit, resulting in poor separation of signals from different tissues or conditions. The problem may be addressed by multiple scans with varying scanning intensities. Multiple scanning intensities raise the question of how to combine different signals from the same spot, particularly when measurement error is not negligible. RESULTS: This paper suggests a non-linear latent regression model for this purpose. It corrects for biases caused by the saturation limit and efficiently combines data from multiple scans. Combining multiple scans also allows reduction of technical error particularly for cDNA spots with low signal. The procedure is exemplified using cDNA expression data from maize. AVAILABILITY: All methods were implemented using standard procedures available in the SAS/STAT module of the SAS System. Programming statements are available from the first author upon request. CONTACT: piepho@uni-hohenheim.de SUPPLEMENTARY INFORMATION: The supplementary data are available at Bioinformatics online.
MOTIVATION: The analysis of spotted cDNA microarrays involves scanning of color signals from fluorescent dyes. A common problem is that a given scanning intensity is not usually optimal for all spotted cDNAs. Specifically, some spots may be at the saturation limit, resulting in poor separation of signals from different tissues or conditions. The problem may be addressed by multiple scans with varying scanning intensities. Multiple scanning intensities raise the question of how to combine different signals from the same spot, particularly when measurement error is not negligible. RESULTS: This paper suggests a non-linear latent regression model for this purpose. It corrects for biases caused by the saturation limit and efficiently combines data from multiple scans. Combining multiple scans also allows reduction of technical error particularly for cDNA spots with low signal. The procedure is exemplified using cDNA expression data from maize. AVAILABILITY: All methods were implemented using standard procedures available in the SAS/STAT module of the SAS System. Programming statements are available from the first author upon request. CONTACT: piepho@uni-hohenheim.de SUPPLEMENTARY INFORMATION: The supplementary data are available at Bioinformatics online.