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OJBTM
Online Journal of Bioinformatics ©
Volume 11 (1): 1-18, 2010.
Beyond single p-value
cut-offs:
Methods to improve decision making in GO enrichment analysis of
microarray
experiments
J.R. de Haan1,
R. Wehrens1,
S. Bauerschmidt2,4, R.C. van Schaik2,4,
E. Piek3,
L.M.C. Buydens1*
2Schering-Plough,
1Institute for Molecules and
Materials, Analytical Chemistry, 3Department
of
Applied Biology, 4Centre for Molecular and Biomolecular
Informatics, Nijmegen Centre for Molecular
Life Sciences, Radboud University
Nijmegen, The Netherlands
ABSTRACT
De Haan
JR, Wehrens R, Bauerschmidt S , Van Schaik
RC, Piek
E, Buydens LMC, Beyond single p-value
cut-offs: Methods to improve decision making in GO enrichment analysis
of
microarray experiments, Online J Bioinformatics, 11
(1):
1-18, 2010. Currently, a large
number
of tools are available to calculate GO enrichment for gene selections
from
microarray experiments. It is well known that this leads to conclusions
that
are dependent on the size of different gene selections. In this paper
we will
investigate this effect by varying the significance level of both the
gene
selection cut-off and the GO enrichment cut-off. A number of techniques
to visualize
the resulting enrichment surface are proposed. Furthermore, ROC plots
are used
to assess the agreement of the experimental results with current
biological
knowledge, such as GO annotation. Using these techniques, a stable
estimate of
association of expression data with GO terms is generated, which is
more robust
than the results of a single test. The methods introduced in this paper
are
illustrated by application to a human mesenchymal
stem cell data set.
Key-Words:
Single p Value, decision making, microarrays