Microarray experimental design: Power and sample size considerations

M. C.K. Yang, J. J. Yang, R. A. McIndoe, J. X. She

Research output: Contribution to journalArticle

39 Citations (Scopus)

Abstract

Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.

Original languageEnglish (US)
Pages (from-to)24-28
Number of pages5
JournalPhysiological Genomics
Volume16
DOIs
StatePublished - Apr 1 2004

Fingerprint

Sample Size
Research Design
Systems Biology
Software
Technology
Gene Expression
Growth
Genes

Keywords

  • Functional genomics
  • Gene expression
  • Statistical analysis

ASJC Scopus subject areas

  • Physiology
  • Genetics

Cite this

Microarray experimental design : Power and sample size considerations. / Yang, M. C.K.; Yang, J. J.; McIndoe, R. A.; She, J. X.

In: Physiological Genomics, Vol. 16, 01.04.2004, p. 24-28.

Research output: Contribution to journalArticle

@article{e311c16af6a1438c8ff2ee996fec654c,
title = "Microarray experimental design: Power and sample size considerations",
abstract = "Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.",
keywords = "Functional genomics, Gene expression, Statistical analysis",
author = "Yang, {M. C.K.} and Yang, {J. J.} and McIndoe, {R. A.} and She, {J. X.}",
year = "2004",
month = "4",
day = "1",
doi = "10.1152/physiolgenomics.00037.2003",
language = "English (US)",
volume = "16",
pages = "24--28",
journal = "Physiological Genomics",
issn = "1094-8341",
publisher = "American Physiological Society",

}

TY - JOUR

T1 - Microarray experimental design

T2 - Power and sample size considerations

AU - Yang, M. C.K.

AU - Yang, J. J.

AU - McIndoe, R. A.

AU - She, J. X.

PY - 2004/4/1

Y1 - 2004/4/1

N2 - Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.

AB - Gene expression analysis using high-throughput microarray technology has become a powerful approach to study systems biology. The exponential growth in microarray experiments has spawned a number of investigations into the reliability and reproducibility of this type of data. However, the sample size requirements necessary to obtain statistically significant results has not had as much attention. We report here statistical methods for the determination of the sufficient number of subjects necessary to minimize the false discovery rate while maintaining high power to detect differentially expressed genes. Two experimental designs were considered: 1) a comparison between two groups at a single time point, and 2) a comparison of two experimental groups with sequential time points. Computer programs are available for the methods discussed in this paper and are adaptable to more complicated situations.

KW - Functional genomics

KW - Gene expression

KW - Statistical analysis

UR - http://www.scopus.com/inward/record.url?scp=1842535253&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=1842535253&partnerID=8YFLogxK

U2 - 10.1152/physiolgenomics.00037.2003

DO - 10.1152/physiolgenomics.00037.2003

M3 - Article

C2 - 14532333

AN - SCOPUS:1842535253

VL - 16

SP - 24

EP - 28

JO - Physiological Genomics

JF - Physiological Genomics

SN - 1094-8341

ER -