Post-Normalization Quality Assessment Visualization of Microarray Data

Post-Normalization Quality Assessment Visualization of Microarray Data

Post-normalization checking of microarrays rarely occurs, despite the problems that using unreliable data for inference can cause. This paper considers a number of different ways to check microarrays after normalization for a variety of potential problems. Four types of problem with microarray data that these checks can identify are: clerical mistakes, array-wide hybridization problems, problem...

cDNA Microarray Data Normalization

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...

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...

Visualization of Cross Platform Microarray Normalization

Background: Combining different microarray data sets together, even across platforms, is considered. The larger sample sizes created in this way have the potential to generally increase statistical power. DWD has been shown to provide this improvement in some cases. But there is an apparent contradiction with the success of the DWD based approach, and earlier analyses by others claiming to show...