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Functionally, we use a simplified competition mechanism at hypothesis level (see Sect. " Competitive hypothesis selection"), which clearly does not capture the details of a fully neuro-dynamic approach (hysteresis, latency behavior,..)..
It has been shown that, in general contexts of gene expression, where gene measurements are correlated within modules, GSA approaches that test "competitive" hypothesis based on "gene sampling models" are anticonservative [10].
" Competitive hypothesis selection".
Various GSEA statistics have been proposed for testing either the competitive hypothesis or self-contained hypothesis.
There are two fundamental hypotheses for GSEA: competitive hypothesis and self-contained hypothesis [ 8].
" Competitive hypothesis selection" Details about processing and classifier training are given in [ 7].
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To tease apart the exact role of competitive hypotheses I propose some experimental designs.
We specifically tested two competitive hypotheses, using two sets of models.
We present competitive hypotheses that either do or do not involve reversal from the traits.
Results can be assigned to the appropriate categories by a process of competitive hypotheses testing.
In this article, we extend the methodological analysis of gene-set methods presented by Goeman and Buehlmann [ 12] who clarified fundamental differences between self-contained and competitive hypotheses, and between subject-sampling and gene-sampling approaches.
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