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 language | English (US) |
|---|---|
| Pages (from-to) | 24-28 |
| Number of pages | 5 |
| Journal | Physiological Genomics |
| Volume | 16 |
| DOIs | |
| State | Published - Apr 1 2004 |
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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 journal › Article
}
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
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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 -