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Microarray Data Analysis (microarray + data_analysis)

Distribution by Scientific Domains

Life Sciences	75%

Selected Abstracts

Statistical Analysis of Microarray Data

ADDICTION BIOLOGY, Issue 1 2005
Mark Reimers
Microarrays promise dynamic snapshots of cell activity, but microarray results are unfortunately not straightforward to interpret. This article aims to distill the most useful practical results from the vast body of literature availalable on microarray data analysis. Topics covered include: experimental design issues, normalization, quality control, exploratory analysis, and tests for differential expression. Special attention is paid to the peculiarities of low-level analysis of Affymetrix chips, and the multiple testing problem in determining differential expression. The aim of this article is to provide useful answers to the most common practical issues in microarray data analysis. The main topics are pre-processing (normalization), and detecting differential expression. Subsidiary topics include experimental design, and exploratory analysis. Further discussion is found at the author's web page (http://discover.nci.nih.gov, Notes on Microarray Data Analysis). [source]

Linear Mixed Model Selection for False Discovery Rate Control in Microarray Data Analysis

BIOMETRICS, Issue 2 2010
Cumhur Yusuf Demirkale
Summary In a microarray experiment, one experimental design is used to obtain expression measures for all genes. One popular analysis method involves fitting the same linear mixed model for each gene, obtaining gene-specific,p -values for tests of interest involving fixed effects, and then choosing a threshold for significance that is intended to control false discovery rate (FDR) at a desired level. When one or more random factors have zero variance components for some genes, the standard practice of fitting the same full linear mixed model for all genes can result in failure to control FDR. We propose a new method that combines results from the fit of full and selected linear mixed models to identify differentially expressed genes and provide FDR control at target levels when the true underlying random effects structure varies across genes. [source]

Statistical Analysis of Microarray Data

DNA Microarrays: Experimental Issues, Data Analysis, and Application to Bacterial Systems

BIOTECHNOLOGY PROGRESS, Issue 5 2004
Yandi Dharmadi
DNA microarrays are currently used to study the transcriptional response of many organisms to genetic and environmental perturbations. Although there is much room for improvement of this technology, its potential has been clearly demonstrated in the past 5 years. The general consensus is that the bottleneck is now located in the processing and analysis of transcriptome data and its use for purposes other than the quantification of changes in gene expression levels. In this article we discuss technological aspects of DNA microarrays, statistical and biological issues pertinent to the design of microarray experiments, and statistical tools for microarray data analysis. A review on applications of DNA microarrays in the study of bacterial systems is presented. Special attention is given to studies in the following areas: (1) bacterial response to environmental changes; (2) gene identification, genome organization, and transcriptional regulation; and (3) genetic and metabolic engineering. Soon, the use of DNA microarray technologies in conjunction with other genome/system-wide analyses (e.g., proteomics, metabolomics, fluxomics, phenomics, etc.) will provide a better assessment of genotype-phenotype relationships in bacteria, which serve as a basis for understanding similar processes in more complex organisms. [source]