Rosetta error model for gene expression analysis
نویسندگان
چکیده
منابع مشابه
Rosetta error model for gene expression analysis
MOTIVATION In microarray gene expression studies, the number of replicated microarrays is usually small because of cost and sample availability, resulting in unreliable variance estimation and thus unreliable statistical hypothesis tests. The unreliable variance estimation is further complicated by the fact that the technology-specific variance is intrinsically intensity-dependent. RESULTS Th...
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متن کاملComments on "Bayesian hierarchical error model for analysis of gene expression data"
Cho and Lee (2004) proposed a Bayesian hierarchical error model (HEM) to account for heterogeneous error variability in oligonucleotide microarray experiments. They estimated the parameters of their model using Markov Chain Monte Carlo (MCMC) and proposed an F-like summary statistic to identify differentially expressed genes under multiple conditions. Their HEM is one of the emerging Bayesian h...
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ژورنال
عنوان ژورنال: Bioinformatics
سال: 2006
ISSN: 1460-2059,1367-4803
DOI: 10.1093/bioinformatics/btl045