804 research outputs found
A framework for list representation, enabling list stabilization through incorporation of gene exchangeabilities
Analysis of multivariate data sets from e.g. microarray studies frequently
results in lists of genes which are associated with some response of interest.
The biological interpretation is often complicated by the statistical
instability of the obtained gene lists with respect to sampling variations,
which may partly be due to the functional redundancy among genes, implying that
multiple genes can play exchangeable roles in the cell. In this paper we use
the concept of exchangeability of random variables to model this functional
redundancy and thereby account for the instability attributable to sampling
variations. We present a flexible framework to incorporate the exchangeability
into the representation of lists. The proposed framework supports
straightforward robust comparison between any two lists. It can also be used to
generate new, more stable gene rankings incorporating more information from the
experimental data. Using a microarray data set from lung cancer patients we
show that the proposed method provides more robust gene rankings than existing
methods with respect to sampling variations, without compromising the
biological significance
A method for visual identification of small sample subgroups and potential biomarkers
In order to find previously unknown subgroups in biomedical data and generate
testable hypotheses, visually guided exploratory analysis can be of tremendous
importance. In this paper we propose a new dissimilarity measure that can be
used within the Multidimensional Scaling framework to obtain a joint
low-dimensional representation of both the samples and variables of a
multivariate data set, thereby providing an alternative to conventional
biplots. In comparison with biplots, the representations obtained by our
approach are particularly useful for exploratory analysis of data sets where
there are small groups of variables sharing unusually high or low values for a
small group of samples.Comment: Published in at http://dx.doi.org/10.1214/11-AOAS460 the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
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