generate_individual_graphs function

Builds graphs from specified network layers

Constructs and returns two graphs for each network layer, where nodes correspond to the rows in the measurement data. Graphs are initially complete and edges are weighted by correlation values of the measurements across columns. The number of edges is then reduced by either a threshold on the p-value of the correlation or a minimum scale-free fit index.

generate_individual_graphs(correlation_matrices, layers, settings)

Arguments

• correlation_matrices: [list] List of correlation matrices generated with codecompute_correlation_matrices
• layers: [list] Named list with different network layers containing data and identifiers for both groups (generated from make_layer)
• settings: [list] A named list containing pipeline settings. The settings list has to be initialized by drdimont_settings. Items in the named list can be adjusted as desired.

Returns

A nested named list with first-level elements graphs and annotations. The second level elements are groupA and groupB (and both at annotations). These contain a list of iGraph objects (graphs) and data frames (annotations) mapping the graph node IDs to biological identifiers. The third level elements are layer names given by the user.

Examples

data(layers_example)
data(correlation_matrices_example)

example_settings <- drdimont_settings(
                        handling_missing_data=list(
                            default="pairwise.complete.obs",
                            mrna="all.obs"),
                        reduction_method="pickHardThreshold",
                        r_squared=list(default=0.65, metabolite=0.1),
                        cut_vector=list(default=seq(0.2, 0.5, 0.01)))

example_individual_graphs <- generate_individual_graphs(
                                 correlation_matrices=correlation_matrices_example,
                                 layers=layers_example, 
                                 settings=example_settings)

graph_metrics(example_individual_graphs$graphs$groupA$mrna)
graph_metrics(example_individual_graphs$graphs$groupB$mrna)