D Wang1, C-H Zhang, M B Soares, J Huang. 1. Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, The University of Alabama, Birmingham, AL 35294, USA. USA. deliwang@uab.edu
Abstract
BACKGROUND: Normalization and data quality control are two important aspects in microarray data analysis. Proper normalization and data quality control ensure that intensity ratios provide meaningful and accurate measurement of relative gene expression values. Control spots such as spikes and housekeeping genes with known concentrations in two channels are often used for calibrating experimental parameters. They provide valuable information about experimental variation which can be utilized for better normalization. They are also needed for proper normalization in cases that the most of the spots tend to change in one direction. In addition, it is desirable to include information on spot quality. Such information is available in a typical microarray data set, but is not fully utilized by existing normalization methods. RESULTS: We propose two extensions of the two-way semi-linear model (TW-SLM) for appropriately combining control genes and spot quality information in normalization. The first extension (TW-SLMC) is designed to systematically incorporate control spots in a semi-parametric model to calibrate estimated normalization curves so that the relative fold changes of gene expressions are accurately estimated. Extrapolation is not required in this approach. The second extension (TW-SLMQ) is proposed to incorporate spot quality measure into normalization. This approach down-weights spots with lower quality scores in normalization. These two extensions can be used simultaneously for normalizing a data set. Two microarray data sets are used to demonstrate the proposed methods. AVAILABILITY: An R based computing package is developed for the proposed methods and available from the corresponding authors.
BACKGROUND: Normalization and data quality control are two important aspects in microarray data analysis. Proper normalization and data quality control ensure that intensity ratios provide meaningful and accurate measurement of relative gene expression values. Control spots such as spikes and housekeeping genes with known concentrations in two channels are often used for calibrating experimental parameters. They provide valuable information about experimental variation which can be utilized for better normalization. They are also needed for proper normalization in cases that the most of the spots tend to change in one direction. In addition, it is desirable to include information on spot quality. Such information is available in a typical microarray data set, but is not fully utilized by existing normalization methods. RESULTS: We propose two extensions of the two-way semi-linear model (TW-SLM) for appropriately combining control genes and spot quality information in normalization. The first extension (TW-SLMC) is designed to systematically incorporate control spots in a semi-parametric model to calibrate estimated normalization curves so that the relative fold changes of gene expressions are accurately estimated. Extrapolation is not required in this approach. The second extension (TW-SLMQ) is proposed to incorporate spot quality measure into normalization. This approach down-weights spots with lower quality scores in normalization. These two extensions can be used simultaneously for normalizing a data set. Two microarray data sets are used to demonstrate the proposed methods. AVAILABILITY: An R based computing package is developed for the proposed methods and available from the corresponding authors.