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      Statistical tests for differential expression in cDNA microarray experiments

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      Author(s): 1 , 1 ,
      Publication date (Electronic):
      Journal: Genome Biology
      Publisher: BioMed Central

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          Abstract

          The simplest statistical method for extracting biological information from microarray data is the t test. Analysis of variance (ANOVA) and the mixed ANOVA model are general and powerful approaches for more complex microarray experiments.

          Abstract

          Extracting biological information from microarray data requires appropriate statistical methods. The simplest statistical method for detecting differential expression is the t test, which can be used to compare two conditions when there is replication of samples. With more than two conditions, analysis of variance (ANOVA) can be used, and the mixed ANOVA model is a general and powerful approach for microarray experiments with multiple factors and/or several sources of variation.

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          Most cited references25

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

          Yoav Benjamini, Yosef Hochberg (1995)
          Article 0 comments Cited 23666 times – based on 0 reviews     Review now
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            Exploring the metabolic and genetic control of gene expression on a genomic scale.

            J. L. DeRisi, V. Iyer, P. O. Brown (1997)
            DNA microarrays containing virtually every gene of Saccharomyces cerevisiae were used to carry out a comprehensive investigation of the temporal program of gene expression accompanying the metabolic shift from fermentation to respiration. The expression profiles observed for genes with known metabolic functions pointed to features of the metabolic reprogramming that occur during the diauxic shift, and the expression patterns of many previously uncharacterized genes provided clues to their possible functions. The same DNA microarrays were also used to identify genes whose expression was affected by deletion of the transcriptional co-repressor TUP1 or overexpression of the transcriptional activator YAP1. These results demonstrate the feasibility and utility of this approach to genomewide exploration of gene expression patterns.
            Article 0 comments Cited 422 times – based on 0 reviews     Review now
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              Computational analysis of microarray data.

              J Quackenbush (2001)
              Microarray experiments are providing unprecedented quantities of genome-wide data on gene-expression patterns. Although this technique has been enthusiastically developed and applied in many biological contexts, the management and analysis of the millions of data points that result from these experiments has received less attention. Sophisticated computational tools are available, but the methods that are used to analyse the data can have a profound influence on the interpretation of the results. A basic understanding of these computational tools is therefore required for optimal experimental design and meaningful data analysis.
              Article 0 comments Cited 256 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): Genome Biol
                Title: Genome Biology
                Publisher: BioMed Central (London )
                ISSN (Print): 1465-6906
                ISSN (Electronic): 1465-6914
                Publication date (Print): 2003
                Publication date (Electronic): 17 March 2003
                Volume: 4
                Issue: 4
                Page: 210
                Affiliations
                [1 ]The Jackson Laboratory, 600 Main Street, Bar Harbor, Maine 04609, USA
                Correspondence: Gary A Churchill. E-mail: garyc@jax.org
                Article
                Publisher ID: gb-2003-4-4-210
                DOI: 10.1186/gb-2003-4-4-210
                PMC ID: 154570
                PubMed ID: 12702200
                SO-VID: 5228f053-9970-43e2-93e9-e9043234aeb5
                Copyright © Copyright © 2003 BioMed Central Ltd
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                Subject: Review

                ScienceOpen disciplines: Genetics
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                ScienceOpen disciplines: Genetics

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