Robust Depth-Based Tools for the Analysis of Gene Expression Data Articles uri icon

publication date

  • April 2010

start page

  • 254

end page

  • 264

issue

  • 2

volume

  • 11

international standard serial number (ISSN)

  • 1465-4644

electronic international standard serial number (EISSN)

  • 1468-4357

abstract

  • Microarray experiments provide data on the expression levels of thousands of genes and, therefore, statistical methods applicable to the analysis of such high-dimensional data are needed. In this paper, we
    propose robust nonparametric tools for the description and analysis of
    microarray data based on the concept of functional depth, which measures
    the centrality of an observation within a sample. We show that this
    concept can be easily adapted to high-dimensional observations and, in
    particular, to gene expression data. This allows the development of the
    following depth-based inference tools: (1) a scale curve for measuring
    and visualizing the dispersion of a set of points, (2) a rank test for
    deciding if 2 groups of multidimensional observations come from the same
    population, and (3) supervised classification techniques for assigning a
    new sample to one of G given groups. We apply these methods to
    microarray data, and to simulated data including contaminated models,
    and show that they are robust, efficient, and competitive with other
    procedures proposed in the literature, outperforming them in some
    situations.