Abstract
In this paper the problem of cell classification using gene expression data is addressed. One of the main features of this kind of data is the very large number of variables (genes), relative to the number of observations (cells). This condition makes most of the standard statistical methods for classification difficult to employ. The proposed solution consists of building classification rules on subsets of genes showing a behavior across the cells that differs most from that of all the other ones. This variable selection procedure is based on suitable linear transformations of the observed data: a strategy resorting to independent component analysis is explored. Our proposal is compared with the nearest shrunken centroid method (Tibshirani et al. (2002)) on three publicly available data sets.
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Calò, D.G., Galimberti, G., Pillati, M., Viroli, C. (2005). Variable Selection in Cell Classification Problems: A Strategy Based on Independent Component Analysis. In: Bock, HH., et al. New Developments in Classification and Data Analysis. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-27373-5_3
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DOI: https://doi.org/10.1007/3-540-27373-5_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-23809-6
Online ISBN: 978-3-540-27373-8
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