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Sequential analysis for microarray data based on sensitivity and meta-analysis
- Source :
- Statistical Applications in Genetics and Molecular Biology, Statistical Applications in Genetics and Molecular Biology, De Gruyter, 2009, 8, online (1), Non paginé. ⟨10.2202/1544-6115.1368⟩
- Publication Year :
- 2009
-
Abstract
- MOTIVATION Transcriptomic studies using microarray technology have become a standard tool in life sciences in the last decade. Nevertheless the cost of these experiments remains high and forces scientists to work with small sample sizes at the expense of statistical power. In many cases, little or no prior knowledge on the underlying variability is available, which would allow an accurate estimation of the number of samples (microarrays) required to answer a particular biological question of interest. We investigate sequential methods, also called group sequential or adaptive designs in the context of clinical trials, for microarray analysis. Through interim analyses at different stages of the experiment and application of a stopping rule a decision can be made as to whether more samples should be studied or whether the experiment has yielded enough information already. RESULTS The high dimensionality of microarray data facilitates the sequential approach. Since thousands of genes simultaneously contribute to the stopping decision, the marginal distribution of any single gene is nearly independent of the global stopping rule. For this reason, the interim analysis does not seriously bias the final p-values. We propose a meta-analysis approach to combining the results of the interim analyses at different stages. We consider stopping rules that are either based on the estimated number of true positives or on a sensitivity estimate and particularly discuss the difficulty of estimating the latter. We study this sequential method in an extensive simulation study and also apply it to several real data sets. The results show that applying sequential methods can reduce the number of microarrays without substantial loss of power. An R-package SequentialMA implementing the approach is available from the authors.
- Subjects :
- Statistics and Probability
Computer science
[SDV]Life Sciences [q-bio]
Context (language use)
computer.software_genre
Statistical power
03 medical and health sciences
Mice
0302 clinical medicine
Meta-Analysis as Topic
Interim
Databases, Genetic
Genetics
Animals
Humans
Computer Simulation
Sensitivity (control systems)
Molecular Biology
030304 developmental biology
Oligonucleotide Array Sequence Analysis
0303 health sciences
Apolipoprotein A-I
Probability and statistics
transcriptomic
Interim analysis
sequential analysis
Computational Mathematics
030220 oncology & carcinogenesis
Gene chip analysis
gene expression
Data mining
Marginal distribution
computer
microarray
meta analysis
Subjects
Details
- ISSN :
- 15446115
- Volume :
- 8
- Database :
- OpenAIRE
- Journal :
- Statistical applications in genetics and molecular biology
- Accession number :
- edsair.doi.dedup.....e5961a3dc483f6ef8829d0a5d70a86d0