DetectiV: visualization, normalization and significance testing for pathogen-detection microarray data

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...

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...

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...

microarray data analysis for detection and classification of viral infection

dna microarrays consist of collection of dna microscopic spots that in order to form an array attached to a solid surface such as glass, plastic or silicon chip. the pieces of fixed dna considered as a searcher. in this technology it is possible to test sample against thousands probes for specific genes. with this ability, arrays accelerate the biological investigations, gene finding, molecular...