2010
DOI: 10.1016/j.jtbi.2010.02.021
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A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference

Abstract: A probabilistic framework for microarray data analysis: Fundamental probability models and statistical inference, Journal of Theoretical Biology, doi:10.1016Biology, doi:10. /j.jtbi.2010 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting galley proof before it is published in its final citable form. Please note t… Show more

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Cited by 7 publications
(13 citation statements)
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References 33 publications
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“…In addition, we conduct several simulation studies based on a widely adopted random effect model used in [15], [36][38]:In this model, represents variation that is the same for every gene and specific to the th array. While it is known that log transformation stabilizes variance for microarray data, more advanced variance stabilization transformation techniques [39], [40] can achieve better uniformity of gene-specific variation.…”
Section: Resultsmentioning
confidence: 99%
“…In addition, we conduct several simulation studies based on a widely adopted random effect model used in [15], [36][38]:In this model, represents variation that is the same for every gene and specific to the th array. While it is known that log transformation stabilizes variance for microarray data, more advanced variance stabilization transformation techniques [39], [40] can achieve better uniformity of gene-specific variation.…”
Section: Resultsmentioning
confidence: 99%
“…A new mixture model method in which transformed signal intensities are characterized by a set of overlapping beta distributions is also a good choice for selecting differentially expressed genes in a two-state experiment (Ogunnaike et al ., 2010). Starting from first principles, the authors showed that this distribution is an adequate descriptor of microarray data and from the set of overlapping fits they calculate the probability that a gene is upregulated, downregulated, or not regulated (Ogunnaike et al ., 2010).…”
Section: Statistical Analysis Of Microarray Resultsmentioning
confidence: 99%
“…Starting from first principles, the authors showed that this distribution is an adequate descriptor of microarray data and from the set of overlapping fits they calculate the probability that a gene is upregulated, downregulated, or not regulated (Ogunnaike et al ., 2010). Replicates can be used to calculate a confidence metric for the reported probabilities (Ogunnaike et al ., 2010).…”
Section: Statistical Analysis Of Microarray Resultsmentioning
confidence: 99%
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