Modeling microarray data using a threshold mixture model

Kauermann G, Eilers P (2004)
BIOMETRICS 60(2): 376-387.

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Zeitschriftenaufsatz | Veröffentlicht | Englisch
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Abstract / Bemerkung
An important goal of microarray studies is the detection of genes that show significant changes in expression when two classes of biological samples are being compared. We present an ANOVA-style mixed model with parameters for array normalization, overall level of gene expression, and change of expression between the classes. For the latter we assume a mixing distribution with a probability mass concentrated at zero, representing genes with no changes, and a normal distribution representing the level of change for the other genes. We estimate the parameters by optimizing the marginal likelihood. To make this practical, Laplace approximations and a backfitting algorithm are used. The performance of the model is studied by simulation and by application to publicly available data sets.
Erscheinungsjahr
Zeitschriftentitel
BIOMETRICS
Band
60
Ausgabe
2
Seite(n)
376-387
ISSN
PUB-ID

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Kauermann G, Eilers P. Modeling microarray data using a threshold mixture model. BIOMETRICS. 2004;60(2):376-387.
Kauermann, G., & Eilers, P. (2004). Modeling microarray data using a threshold mixture model. BIOMETRICS, 60(2), 376-387. doi:10.1111/j.0006-341X.2004.00182.x
Kauermann, G., and Eilers, P. (2004). Modeling microarray data using a threshold mixture model. BIOMETRICS 60, 376-387.
Kauermann, G., & Eilers, P., 2004. Modeling microarray data using a threshold mixture model. BIOMETRICS, 60(2), p 376-387.
G. Kauermann and P. Eilers, “Modeling microarray data using a threshold mixture model”, BIOMETRICS, vol. 60, 2004, pp. 376-387.
Kauermann, G., Eilers, P.: Modeling microarray data using a threshold mixture model. BIOMETRICS. 60, 376-387 (2004).
Kauermann, Göran, and Eilers, P. “Modeling microarray data using a threshold mixture model”. BIOMETRICS 60.2 (2004): 376-387.

8 Zitationen in Europe PMC

Daten bereitgestellt von Europe PubMed Central.

A mixture model approach for the analysis of small exploratory microarray experiments.
Muir WM, Rosa GJ, Pittendrigh BR, Xu S, Rider SD, Fountain M, Ogas J., Comput Stat Data Anal 53(5), 2009
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Dudbridge F, Gusnanto A, Koeleman BP., Hum Genomics 2(5), 2006
PMID: 16595075
Identifying differentially expressed genes in meta-analysis via Bayesian model-based clustering.
Jung YY, Oh MS, Shin DW, Kang SH, Oh HS., Biom J 48(3), 2006
PMID: 16845907

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