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(This article was first published on imDEV » R, and kindly contributed to R-bloggers)

The concept is to make a GUI to provide a static and dynamic linking between data and its network representations.

Static access will involve making networks based on data and metadata stored in some table or spreadsheet.

Dynamic control will provide interactive access to network construction and annotation properties.

Together, these will provide rapid generation of information rich networks, based on tests of internal data properties or from exogenous semantic knowledge. Here is an example of a network representation of a time course metabolomic experiment. This network is used to encode dependence between top parameters of a PLS-DA model discriminating between pre- and post-experimental interventions. Larger nodes show variables meeting the 5% significance cut off (p < 0.05) for a mixed effects model to identify intervention related differences between unbalanced baseline and area under the curve for metabolite excursion measurements during an oral glucose tolerance test (OGTT). Node color signifies increase (red) or decrease (blue) in post- relative to pre-intervention average values. Node shape and outline display metabolite classification and presence in a PLS-DA model respectively. Node graphs, created in ggplots2, show box plots for pre- (red) and post-intervention (green) class distribution medians, upper and lower quartiles, and outliers.

The interactions between model parameters which exist only in pre-intervention samples are shown in the network below.

Connections are made between metabolites which have a non-zero partial correlation extracted based on a qpnetwork trimmed at a threshold where node and edge number is ~equal. In this network all edges meet the 5% significance based on tests of persons correlations.

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