A Semi-linear Model for Normalization and Analysis of cDNA Microarray Data
نویسندگان
چکیده
Motivation: Microarray analysis is a technology for monitoring gene expression levels on a large scale and has been widely used in functional genomics. A challenging issue in the analysis of microarray data is normalization. A proper normalization procedure ensures that the intensity ratios provide meaningful measures of relative expression levels. There are two important questions concerning normalization not adequately addressed in the current literature: (a) how to identify genes that have constant expression levels in order to establish the normalization curves; (b) how to account for the uncertainty inherent in the normalization process in the subsequent statistical analysis. Results: We propose a semi-linear model that incorporates normalization into the analysis. This method does not make the usual assumptions needed for the loess and dye-swap normalization procedures, nor does it require to identify a set of constantly expressed genes prior to normalization. It also naturally accounts for the uncertainty in the normalization process. We apply the proposed method to two microarray data sets to illustrate this approach and its differences from the loess normalization method. Availability: A set of programs will be electronically sent upon request. Contact: [email protected]
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تاریخ انتشار 2003