期刊
JOURNAL OF COMPUTATIONAL BIOLOGY
卷 8, 期 1, 页码 37-52出版社
MARY ANN LIEBERT INC PUBL
DOI: 10.1089/106652701300099074
关键词
empirical Bayesian analysis; global gene expression; hierarchical modeling
类别
资金
- NCI NIH HHS [R29CA64364-01, TA-CA 09565] Funding Source: Medline
- NIGMS NIH HHS [R01 GM35682] Funding Source: Medline
- NATIONAL CANCER INSTITUTE [T32CA009565, R29CA064364] Funding Source: NIH RePORTER
- NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES [R01GM035682] Funding Source: NIH RePORTER
We consider the problem of inferring fold changes in gene expression from cDNA microarray data. Standard procedures focus on the ratio of measured fluorescent intensities at each spot on the microarray, but to do so is to ignore the fact that the variation of such ratios is not constant. Estimates of gene expression changes are derived within a simple hierarchical model that accounts for measurement error and fluctuations in absolute gene expression levels. Significant gene expression changes are identified by deriving the posterior odds of change within a similar model. The methods are tested via simulation and are applied to a panel of Escherichia coli microarrays.
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