Literature DB >> 12935350

Microarrays: how many do you need?

Alexander Zien1, Juliane Fluck, Ralf Zimmer, Thomas Lengauer.   

Abstract

We estimate the number of microarrays that is required in order to gain reliable results from a common type of study: the pairwise comparison of different classes of samples. We show that current knowledge allows for the construction of models that look realistic with respect to searches for individual differentially expressed genes and derive prototypical parameters from real data sets. Such models allow investigation of the dependence of the required number of samples on the relevant parameters: the biological variability of the samples within each class, the fold changes in expression that are desired to be detected, the detection sensitivity of the microarrays, and the acceptable error rates of the results. We supply experimentalists with general conclusions as well as a freely accessible Java applet at www.scai.fhg.de/special/bio/howmanyarrays/ for fine tuning simulations to their particular settings.

Mesh:

Year:  2003        PMID: 12935350     DOI: 10.1089/10665270360688246

Source DB:  PubMed          Journal:  J Comput Biol        ISSN: 1066-5277            Impact factor:   1.479


  14 in total

Review 1.  Associating phenotypes with molecular events: recent statistical advances and challenges underpinning microarray experiments.

Authors:  Yulan Liang; Arpad Kelemen
Journal:  Funct Integr Genomics       Date:  2005-11-15       Impact factor: 3.410

2.  Gene expression profiling in the rhesus macaque: methodology, annotation and data interpretation.

Authors:  Nigel C Noriega; Steven G Kohama; Henryk F Urbanski
Journal:  Methods       Date:  2009-05-23       Impact factor: 3.608

3.  Kidney transplant rejection and tissue injury by gene profiling of biopsies and peripheral blood lymphocytes.

Authors:  Stuart M Flechner; Sunil M Kurian; Steven R Head; Starlette M Sharp; Thomas C Whisenant; Jie Zhang; Jeffrey D Chismar; Steve Horvath; Tony Mondala; Timothy Gilmartin; Daniel J Cook; Steven A Kay; John R Walker; Daniel R Salomon
Journal:  Am J Transplant       Date:  2004-09       Impact factor: 8.086

Review 4.  Challenges and approaches to statistical design and inference in high-dimensional investigations.

Authors:  Gary L Gadbury; Karen A Garrett; David B Allison
Journal:  Methods Mol Biol       Date:  2009

5.  Independent and functional validation of a multi-tumour-type proliferation signature.

Authors:  M H W Starmans; N G Lieuwes; P N Span; S Haider; L Dubois; F Nguyen; H W van Laarhoven; F C G J Sweep; B G Wouters; P C Boutros; P Lambin
Journal:  Br J Cancer       Date:  2012-06-21       Impact factor: 7.640

6.  Skeletal muscle gene expression in response to resistance exercise: sex specific regulation.

Authors:  Dongmei Liu; Maureen A Sartor; Gustavo A Nader; Laurie Gutmann; Mary K Treutelaar; Emidio E Pistilli; Heidi B Iglayreger; Charles F Burant; Eric P Hoffman; Paul M Gordon
Journal:  BMC Genomics       Date:  2010-11-24       Impact factor: 3.969

7.  A simple but highly effective approach to evaluate the prognostic performance of gene expression signatures.

Authors:  Maud H W Starmans; Glenn Fung; Harald Steck; Bradly G Wouters; Philippe Lambin
Journal:  PLoS One       Date:  2011-12-07       Impact factor: 3.240

8.  Transformation of expression intensities across generations of Affymetrix microarrays using sequence matching and regression modeling.

Authors:  Soumyaroop Bhattacharya; Thomas J Mariani
Journal:  Nucleic Acids Res       Date:  2005-10-13       Impact factor: 16.971

9.  Sequential stopping for high-throughput experiments.

Authors:  David Rossell; Peter Müller
Journal:  Biostatistics       Date:  2012-08-20       Impact factor: 5.899

10.  Improving identification of differentially expressed genes in microarray studies using information from public databases.

Authors:  Richard D Kim; Peter J Park
Journal:  Genome Biol       Date:  2004-08-26       Impact factor: 13.583

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