Modeling microarray data using a threshold mixture model

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

Journal Article | Published | English

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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.
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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.
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.
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