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Date : 2003-03-26
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Statistical Analysis of Gene Expression Microarray Data ~ Statistical Analysis of Gene Expression Microarray Data promises to become the definitive basic reference in the field Under the editorship of Terry Speed some of the worlds most preeminent authorities have joined forces to present the tools features and problems associated with the analysis of genetic microarray data
Gene expression Microarray data analysis ~ Microarrays tools for gene expression The most common form of microarray is used to measure gene expression RNA is isolated from matched samples of interest The RNA is typically converted to cDNA labeled with fluorescence or radioactivity then hybridized to microarrays in order to measure the expression levels of thousands of genes
Statistical Design and the Analysis of Gene Expression ~ Statistical Design and the Analysis of Gene Expression Microarray Data1 M Kathleen Kerr and Gary A Churchill The Jackson Laboratory Bar Harbor Maine 04609 USA Short title Statistical Design and Analysis of Microarrays Proofs to be sent to M Kathleen Kerr The Jackson Laboratory 600 Main Street Box 303 Bar
Statistical analysis of gene expression data ~ six biomarkers for estrogen exposure in fish are found using both an inhouse microarray experiment and a metaanalysis of several public gene expression datasets Keywords gene expression DNA microarrays linear models empirical Bayes quality control gene regulation categorical data analysis logistic regression heavy metal stress
Statistical Methods in Microarray Gene Expression Data ~ Summary Microarrays allow the simultaneous measurement of the expression levels of thousands of genes This unique data structure has inspired a completely new area of research in statistics and bioinformatics The objective of the present chapter is to review some of the main statistical tools used in this context
Getting Started in Gene Expression Microarray Analysis ~ One common strategy is to create a custom data analysis pipeline using statistical analysis software packages such as Matlab or R Both allow great flexibility customized analysis and access to many specialized packages designed for analyzing gene expression data
Analysis of microarray experiments of gene expression ~ A major problem in the analysis of microarray data is that many hypotheses are tested simultaneously More precisely testing the differential expression of each gene in the array involves one hypothesis The number of genes represented in a commercially available array is on the order of tens of thousands
Microarray Data Analysis and Mining Tools ~ Metabolic pathway analysis can identify more subtle changes in expression than the gene lists that result from univariate statistical analysis There are several web based tools and academic softwares are available to predict protein interactions and pathways from microarray data and are tabulated in Table 5 see Table 5
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