File Name: normalization of microarray data single labeled and dual labeled arrays .zip
DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray ex-periments. Normalization is a critical step for obtaining data that are reliable and usable for subsequent analysis such as identification of differentially expressed genes and clustering. A variety of normalization methods have been proposed over the past few years, but no methods are still perfect. Various assumptions are often taken in the process of normalization. Therefore, the knowledge of underlying assumption and principle of normaliza-tion would be helpful for the correct analysis of mi-croarray data. We present a review of normalization techniques from single-labeled platforms such as the Affymetrix GeneChip array to dual-labeled platforms like spotted array focusing on their principles and as-sumptions.
Normalization is a critical step in analysis of gene expression profiles. For dual-labeled arrays, global normalization assumes that the majority of the genes on the array are non-differentially expressed between the two channels and that the number of over-expressed genes approximately equals the number of under-expressed genes. These assumptions can be inappropriate for custom arrays or arrays in which the reference RNA is very different from the experimental samples. We propose a mixture model based normalization method that adaptively identifies non-differentially expressed genes and thereby substantially improves normalization for dual-labeled arrays in settings where the assumptions of global normalization are problematic. The new method is evaluated using both simulated and real data.
Skip to search form Skip to main content You are currently offline. Some features of the site may not work correctly. Corpus ID: Normalization of microarray data: single-labeled and dual-labeled arrays. Do and D. Choi Published Biology, Medicine Molecules and cells. DNA microarray is a powerful tool for high-throughput analysis of biological systems. Various computational tools have been created to facilitate the analysis of the large volume of data produced in DNA microarray experiments.
Nowadays, microarray gene expression analysis is a widely used technology that scientists handle but whose final interpretation usually requires the participation of a specialist. The need for this participation is due to the requirement of some background in statistics that most users lack or have a very vague notion of. Moreover, programming skills could also be essential to analyse these data. An interactive, easy to use application seems therefore necessary to help researchers to extract full information from data and analyse them in a simple, powerful and confident way. All of this gives support to scientists that have been using previous PreP releases since its first version in Large scale gene expression monitoring technology is changing our view of the biological processes, including their dynamics.
Systematic and gene-specific dye bias effects have been observed in dual-color experiments. In contrast to systematic effects which can be corrected by a normalization method, the gene-specific dye bias is not completely suppressed and may alter the conclusions about the differentially expressed genes. Methods: The gene-specific dye bias is taken into account using an analysis of variance model. We propose an index, named label bias index, to measure the gene-specific dye bias. It requires at least two self—self hybridization cDNA microarrays.
Metrics details. The goal of this study was, first, to assess the quality of miRNA expression data based on microarray technologies and, second, to consolidate it by applying a novel normalization method. Indeed, because of significant differences in platform designs, miRNA raw data cannot be normalized blindly with standard methods developed for gene expression.
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Keywords: loess/lowess; Normalization; RMA; Single- labeled and Dual-labeled Arrays. Introduction. Microarray technology allows investigators to obtain.
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