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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/19660

Title: Modeling of bioassay data and genes expression in drug discovery experiments: A supervised principal component analysis approach
Authors: Bigirumurame, Theophile
Perualila, Nolen Joy
Shkedy, Ziv
Kasim, Adetayo
Issue Date: 2014
Citation: Non Clinical Statistics 2014, Brugge, Belgium, October 8-10, 2014
Abstract: Nowadays, microarray technology is extensively used in biological and medical studies to monitor simultaneously the activity of thousands genes and their response to a certain treatment. In some experiment, in addition to gene expression, some responses are available and the question of interest is to identify whether or not the gene expressions can serve as biomarkers, and perhaps, as surrogate for the response. However, it is challenging to analyze the microarray data due to high dimensionality of the data and relatively small number of observations. Moreover, although the number of genes assayed is large, there may be only a small number of biomarkers that are associated with variations of the outcome. Thus, selection of genes, for which gene expression might serve as biomarker for the outcome, is needed. In our study supervised principal component analysis has been used to construct a joint biomarker. A subset of genes that are associated with the outcome is selected, and is used to build the joint biomarker.
URI: http://hdl.handle.net/1942/19660
Category: C2
Type: Conference Material
Appears in Collections: Research publications

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