Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

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Scanning microarrays at multiple intensities enhances discovery of differentially expressed genes

MOTIVATION Scanning parameters are often overlooked when optimizing microarray experiments. A scanning approach that extends the dynamic data range by acquiring multiple scans of different intensities has been developed. RESULTS Data from each of three scan intensities (low, medium, high) were analyzed separately using multiple scan and linear regression approaches to identify and compare the...

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DNA microarrays open up a broad new horizon for investigators interested in studying the genetic determinants of disease. The high throughputnature of these arrays, where differential expression for thousands of genes can bemeasured simultaneously, creates an enormous wealth of information, but also poses a challenge for data analysis because of the large multiple testing problem involved. The ...

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Mixture Models for Detecting Differentially Expressed Genes in Microarrays

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the mult...

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Microarray data are used to determine which genes are active in response to a changing cell environment. Genes are "discovered" when they are significantly differentially expressed in the microarray data collected under the differing conditions. In one prevalent approach, all genes are assumed to satisfy a null hypothesis, ℍ 0, of no difference in expression. A false discovery (type 1 error) oc...

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Identifying differentially expressed genes using false discovery rate controlling procedures

MOTIVATION DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co...

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2006

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btl270