Iterative rank-order normalization of gene expression microarray data

cDNA Microarray Data Normalization

Normalization and Differential Gene Expression Analysis of Microarray Data

DNA microarray technologies have the capability of simultaneously measuring the abundance of thousands of mRNA-sequences. Analysis of microarray data involves many different steps such as image analysis, background correction, and normaliza-tion, but also more classical statistical analysis such as testing for significant differences between groups of arrays. The work presented in this thesis i...

Normalization methods for analysis of microarray gene-expression data.

This paper investigates subset normalization to adjust for location biases (e.g., splotches) combined with global normalization for intensity biases (e.g., saturation). A data set from a toxicogenomic experiment using the same control and the same treated sample hybridized to six different microarrays is used to contrast the different normalization methods. Simple t-tests were used to compare t...

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...

Normalization of Microarray Data

DNA microarrays are a powerful technology for analysis of gene expression levels within cells. Both the cDNA and oligonucleotide technologies for microarrays are fairly young and are prone to a broad collection of errors and inaccuracies [KYML02]. In this paper we briefly discuss some sources of error and detail a variety of methods for normalizing the data with respect to some of these error s...