Model selection and efficiency testing for normalization of cDNA microarray data by iterative local regression

OLIN: optimized normalization, visualization and quality testing of two-channel microarray data

UNLABELLED Microarray data are generated in complex experiments and frequently compromised by a variety of systematic errors. Subsequent data normalization aims to correct these errors. Although several normalization methods have recently been proposed, they frequently fail to account for the variability of systematic errors within and between microarray experiments. However, optimal adjustment...

cDNA Microarray Data Normalization

Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation.

There are many sources of systematic variation in cDNA microarray experiments which affect the measured gene expression levels (e.g. differences in labeling efficiency between the two fluorescent dyes). The term normalization refers to the process of removing such variation. A constant adjustment is often used to force the distribution of the intensity log ratios to have a median of zero for ea...

An Iterative Nonlinear Regression Method for Microarray Data Normalization

Normalization is a prerequisite for almost all follow-up steps in microarray data analysis. Accurate normalization across different experiments and phenotypes assures a common base for comparative yet quantitative studies using gene expression data. In this paper, we report a novel normalization approach, namely iterative nonlinear regression (INR) method, which exploits concurrent identificati...

Spatial Analysis of cDNA Microarray Experiments

Some normalization methods assume that the two channels of intensities are related by a constant so that Microarray experiments allow RNA level measurements for many the center of the distribution of the log of the ratio is genes in multiple samples. However, mining the biological information shifted to zero. Chen et al. (1997) proposed an iterative from the large sets of data generated by micr...