共 35 条
Detecting differential gene expression with a semiparametric hierarchical mixture method
被引:400
作者:
Newton, MA
Noueiry, A
Sarkar, D
Ahlquist, P
机构:
[1] Univ Wisconsin, Dept Stat, Madison, WI 53706 USA
[2] Univ Wisconsin, Dept Biostat & Med Informat, Madison, WI 53792 USA
[3] Univ Wisconsin, Inst Mol Virol, Madison, WI 53706 USA
[4] Univ Wisconsin, Inst Mol Virol, Madison, WI 53706 USA
关键词:
empirical Bayes;
false discovery rate;
microarrays;
ordered alternatives;
posterior probability;
statistical analysis;
D O I:
10.1093/biostatistics/5.2.155
中图分类号:
Q [生物科学];
学科分类号:
07 ;
0710 ;
09 ;
摘要:
Mixture modeling provides an effective approach to the differential expression problem in microarray data analysis. Methods based on fully parametric mixture models are available, but lack of fit in some examples indicates that more flexible models may be beneficial. Existing, more flexible, mixture models work at the level of one-dimensional gene-specific summary statistics, and so when there are relatively few measurements per gene these methods may not provide sensitive detectors of differential expression. We propose a hierarchical mixture model to provide methodology that is both sensitive in detecting differential expression and sufficiently flexible to account for the complex variability of normalized microarray data. EM-based algorithms are used to fit both parametric and semiparametric versions of the model. We restrict attention to the two-sample comparison problem; an experiment involving Affymetrix microarrays and yeast translation provides the motivating case study. Gene-specific posterior probabilities of differential expression form the basis of statistical inference; they define short gene lists and false discovery rates. Compared to several competing methodologies, the proposed methodology exhibits good operating characteristics in a simulation study, on the analysis of spike-in data, and in a cross-validation calculation.
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页码:155 / 176
页数:22
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