Microarray technology is a powerful approach for genomics research. The multi-step, data-intensive nature of this technology has created an unprecedented informatics and analytical challenge. It is important to understand the crucial steps that can affect the outcome of the analysis. In this review, we provide an overview of the contemporary trend on various main analysis steps in the microarra...

Microarray expression analysis has become one of the most widely used functional genomics tools. Efficient application of this technique requires the development of robust and reproducible protocols. We have optimized all aspects of the process, including PCR amplification of target cDNA clones, microarray printing, probe labeling, and hybridization, and we have developed strategies for data no...

Pre-processing plays a vital role in cDNA-microarray data analysis. Without proper preprocessing it is likely that the biological conclusions will be misleading. However, there are many alternatives and in order to choose a proper pre-processing procedure it is necessary to understand the effect of different methods. This chapter discusses several pre-processing steps, including image analysis,...

We consider cDNA microarray experiments when the cell populations have a factorial structure, and investigate the problem of their optimal designing under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parametrization. First, analytical results are given for the 2× 2 factorial. Since practical applications often involve a more complex...

Complementary DNA (cDNA) microarray is a well-established tool for simultaneously studying the expression level of thousands of genes. Segmentation of microarray images is one of the main stages in a microarray experiment. However, it remains an arduous and challenging task due to the poor quality of images. Images suffer from noise, artifacts, and uneven background, while spots depicted on ima...

Statistical considerations are frequently to the fore in the analysis of microarray data, as researchers sift through massive amounts of data and adjust for various sources of variability in order to identify the important genes amongst the many which are measured. This article summarizes some of the issues involved and provides a brief review of the analysis tools which are available to resear...

The segmentation of the picture (and, implicitly, the segmentation method used) is one of the three important steps in microarray image processing, together with spot gridding and information extraction. It directly affects the accuracy of gene expression analysis in the data mining process that follows. In 2001 a segmentation method for arbitrary pictures using the Active Contours Without Edge...

As each set of microarray data is affected by variations in experimental conditions, appropriate normalization processes are required. Various approaches towards such normalization have been proposed and generally involve adjustments to every pair of the data sets, often between the simultaneously hybridizing R and G probes. Chen et al. [1] introduced a model in which each the signal ratio betw...

MOTIVATION A major focus of current cancer research is to identify genes that can be used as markers for prognosis and diagnosis, and as targets for therapy. Microarray technology has been applied extensively for this purpose, even though it has been reported that the agreement between microarray platforms is poor. A critical question is: how can we best combine the measurements of matched gene...

Gene microarray technology permits quantitative measurement and gene expression profiling of transcript levels on a genome-wide basis. Gene microarray technology is used in numerous biological disciplines in a variety of applications including global gene expression analysis in relation to developmental stage, to a disease state, and in toxic responses. Herein, we include a demonstration of glo...