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In an analysis of the data from both interactive and non-interactive modules, repeated measures ANOVAs indicated significant knowledge gain from pretest to posttest for both interactive and non-interactive formats for each module.
Two-way analyses of variance (ANOVA) with repeated measures (pre/posttest by interactive/non-interactive format) indicated significant knowledge gains from pretest to posttest (p < 0.002 for all six modules).
GO analysis of these overlapped module genes indicate significant over-representation of translation, RNA processing, generation of precursor metabolites and energy.
To reveal hidden direct correlations between modules, i.e correlations masked by the effect of other modules, we adopted partial correlation analysis and constructed a network based on Gaussian graphical model where nodes correspond to modules and edges indicate significant partial correlations between modules.
Significant and positive correlations between members within the modules indicated they may co-occur with mutualism interactions, such as an exchange of metabolic intermediates.
Hence, the large fraction of significant modules indicates that our division into modules based on common motifs and co-expression is indeed relevant.
Asterisks indicate significant differences (**, P<0.01; t-test).
Asterisks indicate significant difference vs. naïve mice.
* indicates significant difference at p < 0.05.
This indicates significant cell-to-cell variability.
* indicates significant odds ratio.
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