A flexible two-stage procedure for identifying gene sets that are differentially expressed

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A flexible two-stage procedure for identifying gene sets that are differentially expressed

MOTIVATION Microarray data analysis has expanded from testing individual genes for differential expression to testing gene sets for differential expression. The tests at the gene set level may focus on multivariate expression changes or on the differential expression of at least one gene in the gene set. These tests may be powerful at detecting subtle changes in expression, but findings at the ...

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Enhancing Hotelling's T2 Statistic using Shrinkage Covariance Matrix for Identifying Differentially Expressed Gene Sets

The breakthrough of microarray technology is a vital research instrument to measure the quantitative and highly parallel of gene expression. In microarray studies, it is common that the data set typically consists of tens of thousands of genes (variables) from just dozens of samples due to various constraints including the high cost of producing microarray chips. As a result, the combined sampl...

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Multivariate methods for identifying differentially expressed genes

Motivation: Univariate testing procedures remain the most common way to identify differentially expressed genes (DEGs). Univariate techniques suffer from the multiple comparison problem and reduced power, because they fail to account for gene interaction. Motivated by these issues, we adopt a multivariate procedure. Namely, we utilize the sup-norm test, which was specifically developed for high...

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Rainbow trout ( Oncorhynchus mykiss) possess two somatostatin mRNAs that are differentially expressed.

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ژورنال

عنوان ژورنال: Bioinformatics

سال: 2009

ISSN: 1367-4803,1460-2059

DOI: 10.1093/bioinformatics/btp076