Literature DB >> 17229587

Understanding sample size: what determines the required number of microarrays for an experiment?

Tommy S Jørstad1, Mette Langaas, Atle M Bones.   

Abstract

DNA microarray experiments have become a widely used tool for studying gene expression. An important, but difficult, part of these experiments is deciding on the appropriate number of biological replicates to use. Often, researchers will want a number of replicates that give sufficient power to recognize regulated genes while controlling the false discovery rate (FDR) at an acceptable level. Recent advances in statistical methodology can now help to resolve this issue. Before using such methods it is helpful to understand the reasoning behind them. In this Research Focus article we explain, in an intuitive way, the effect sample size has on the FDR and power, and then briefly survey some recently proposed methods in this field of research and provide an example of use.

Mesh:

Year:  2007        PMID: 17229587     DOI: 10.1016/j.tplants.2007.01.001

Source DB:  PubMed          Journal:  Trends Plant Sci        ISSN: 1360-1385            Impact factor:   18.313


  12 in total

1.  Transcriptional regulatory networks in Arabidopsis thaliana during single and combined stresses.

Authors:  Pankaj Barah; Mahantesha Naika B N; Naresh Doni Jayavelu; Ramanathan Sowdhamini; Khader Shameer; Atle M Bones
Journal:  Nucleic Acids Res       Date:  2015-12-17       Impact factor: 16.971

2.  Genome-wide gene expression profiles in response to plastid division perturbations.

Authors:  Jodi Maple; Per Winge; Astrid Elisabeth Tveitaskog; Daniela Gargano; Atle M Bones; Simon Geir Møller
Journal:  Planta       Date:  2011-06-29       Impact factor: 4.116

3.  BABAR: an R package to simplify the normalisation of common reference design microarray-based transcriptomic datasets.

Authors:  Mark J Alston; John Seers; Jay C D Hinton; Sacha Lucchini
Journal:  BMC Bioinformatics       Date:  2010-02-03       Impact factor: 3.169

4.  Preferred analysis methods for Affymetrix GeneChips. II. An expanded, balanced, wholly-defined spike-in dataset.

Authors:  Qianqian Zhu; Jeffrey C Miecznikowski; Marc S Halfon
Journal:  BMC Bioinformatics       Date:  2010-05-27       Impact factor: 3.169

5.  Gene transcription in Lactarius quietus-Quercus petraea ectomycorrhizas from a forest soil.

Authors:  P E Courty; M Poletto; F Duchaussoy; M Buée; J Garbaye; F Martin
Journal:  Appl Environ Microbiol       Date:  2008-09-12       Impact factor: 4.792

6.  Application of the Gini correlation coefficient to infer regulatory relationships in transcriptome analysis.

Authors:  Chuang Ma; Xiangfeng Wang
Journal:  Plant Physiol       Date:  2012-07-13       Impact factor: 8.340

7.  Testing the importance of jasmonate signalling in induction of plant defences upon cabbage aphid (Brevicoryne brassicae) attack.

Authors:  Anna Kuśnierczyk; Diem H T Tran; Per Winge; Tommy S Jørstad; John C Reese; Joanna Troczyńska; Atle M Bones
Journal:  BMC Genomics       Date:  2011-08-19       Impact factor: 3.969

8.  Molecular signatures in Arabidopsis thaliana in response to insect attack and bacterial infection.

Authors:  Pankaj Barah; Per Winge; Anna Kusnierczyk; Diem Hong Tran; Atle M Bones
Journal:  PLoS One       Date:  2013-03-25       Impact factor: 3.240

9.  A mixture model approach to sample size estimation in two-sample comparative microarray experiments.

Authors:  Tommy S Jørstad; Herman Midelfart; Atle M Bones
Journal:  BMC Bioinformatics       Date:  2008-02-25       Impact factor: 3.169

10.  The Effect of Acute and Chronic Social Stress on the Hippocampal Transcriptome in Mice.

Authors:  Adrian M Stankiewicz; Joanna Goscik; Alicja Majewska; Artur H Swiergiel; Grzegorz R Juszczak
Journal:  PLoS One       Date:  2015-11-10       Impact factor: 3.240

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