Literature DB >> 12967957

The effect of replication on gene expression microarray experiments.

Paul Pavlidis1, Qinghong Li, William Stafford Noble.   

Abstract

MOTIVATION: We examine the effect of replication on the detection of apparently differentially expressed genes in gene expression microarray experiments. Our analysis is based on a random sampling approach using real data sets from 16 published studies. We consider both the ability to find genes that meet particular statistical criteria as well as the stability of the results in the face of changing levels of replication.
RESULTS: While dependent on the data source, our findings suggest that stable results are typically not obtained until at least five biological replicates have been used. Conversely, for most studies, 10-15 replicates yield results that are quite stable, and there is less improvement in stability as the number of replicates is further increased. Our methods will be of use in evaluating existing data sets and in helping to design new studies.

Mesh:

Year:  2003        PMID: 12967957     DOI: 10.1093/bioinformatics/btg227

Source DB:  PubMed          Journal:  Bioinformatics        ISSN: 1367-4803            Impact factor:   6.937


  50 in total

1.  A dynamic, web-accessible resource to process raw microarray scan data into consolidated gene expression values: importance of replication.

Authors:  Nolwenn Le Meur; Guillaume Lamirault; Audrey Bihouée; Marja Steenman; Hélène Bédrine-Ferran; Raluca Teusan; Gérard Ramstein; Jean J Léger
Journal:  Nucleic Acids Res       Date:  2004-10-08       Impact factor: 16.971

2.  Male sex interspecies divergence and down regulation of expression of spermatogenesis genes in Drosophila sterile hybrids.

Authors:  Vignesh Sundararajan; Alberto Civetta
Journal:  J Mol Evol       Date:  2010-11-16       Impact factor: 2.395

3.  Comparison of transcript profiling on Arabidopsis microarray platform technologies.

Authors:  Jeffrey D Pylatuik; Pierre R Fobert
Journal:  Plant Mol Biol       Date:  2005-07       Impact factor: 4.076

Review 4.  Utility of correlation measures in analysis of gene expression.

Authors:  Anthony Almudevar; Lev B Klebanov; Xing Qiu; Peter Salzman; Andrei Y Yakovlev
Journal:  NeuroRx       Date:  2006-07

Review 5.  The microarray data analysis process: from raw data to biological significance.

Authors:  N Eric Olson
Journal:  NeuroRx       Date:  2006-07

Review 6.  Validation and quality control of protein microarray-based analytical methods.

Authors:  Larry J Kricka; Stephen R Master
Journal:  Mol Biotechnol       Date:  2007-08-03       Impact factor: 2.695

7.  Gene expression profile in pelvic organ prolapse.

Authors:  S S Brizzolara; J Killeen; J Urschitz
Journal:  Mol Hum Reprod       Date:  2008-12-04       Impact factor: 4.025

8.  Amniotic fluid RNA gene expression profiling provides insights into the phenotype of Turner syndrome.

Authors:  Lauren J Massingham; Kirby L Johnson; Thomas M Scholl; Donna K Slonim; Heather C Wick; Diana W Bianchi
Journal:  Hum Genet       Date:  2014-05-22       Impact factor: 4.132

9.  Importance of replication in analyzing time-series gene expression data: corticosteroid dynamics and circadian patterns in rat liver.

Authors:  Tung T Nguyen; Richard R Almon; Debra C DuBois; William J Jusko; Ioannis P Androulakis
Journal:  BMC Bioinformatics       Date:  2010-05-26       Impact factor: 3.169

10.  Internal standard-based analysis of microarray data. Part 1: analysis of differential gene expressions.

Authors:  Igor Dozmorov; Ivan Lefkovits
Journal:  Nucleic Acids Res       Date:  2009-08-31       Impact factor: 16.971

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.