New Normalization Methods for CDNA Microarray Data

cDNA Microarray Data Normalization

Normalization of cDNA Microarray Data By Using Neural Networks

In microarray experiments, there are a variety of systematic errors to affect the measured gene expression levels. Although a number of algorithms were proposed for the normalization of different type of cDNA microarray data, they have encountered many difficulties due to the complex nonlinear sources of systematic error. In this case, a nonlinear normalization method is of great potential to d...

Identification and Normalization of Plate Effects in Cdna Microarray Data

Introducing a new way of visualizing cDNA microarray data we have identified a new type of systematic variation, which we refer to as plate effects. We believe that plate effects are due to non-biological differences in the cDNA clones products spotted onto the microarray slides. By comparing the consistency of all replicates (both within and between slides) after performing 42 different normal...

Bioconductor R packages for exploratory analysis and normalization of cDNA microarray data

This chapter describes a collection of four R packages for exploratory analysis and normalization of two–color cDNA microarray fluorescence intensity data. R’s object–oriented class/method mechanism is exploited to allow efficient and systematic representation and manipulation of large microarray datasets of multiple types. The marrayClasses package contains class definitions and associated met...

A new outlier removal approach for cDNA microarray normalization.

Normalization is a critical step in the analysis of microarray gene expression data. For dual-labeled array, traditional normalization methods assume that the majority of genes are non-differentially expressed and that the number of overexpressed genes approximately equals the number of under-expressed genes. However, these assumptions are inappropriate in some particular conditions. Differenti...