Calculates a global test for differential gene expression
between groups
U. Mansmann, R. Meister, M. Hummel, R. Scheufele, with
        contributions from S. Knueppel
We give the following arguments in support of the
        GlobalAncova approach: After appropriate normalisation,
        gene-expression-data appear rather symmetrical and outliers are
        no real problem, so least squares should be rather robust.
        ANCOVA with interaction yields saturated data modelling e.g.
        different means per group and gene. Covariate adjustment can
        help to correct for possible selection bias. Variance
        homogeneity and uncorrelated residuals cannot be expected.
        Application of ordinary least squares gives unbiased, but no
        longer optimal estimates (Gauss-Markov-Aitken). Therefore,
        using the classical F-test is inappropriate, due to
        correlation. The test statistic however mirrors deviations from
        the null hypothesis. In combination with a permutation
        approach, empirical significance levels can be approximated.
        Alternatively, an approximation yields asymptotic p-values.
        This work was supported by the NGFN grant 01 GR 0459, BMBF,
        Germany.
Biobase, annotate, GO.db, KEGG.db, golubEsets, hu6800.db,
vsn, GSEABase, Rgraphviz