compute_correlation_matrices function

Computes correlation matrices for specified network layers

Constructs and returns a correlation/adjacency matrices for each network layer and each group. The adjacency matrix of correlations is computed using cor . The handling of missing data can be specified. Optionally, the adjacency matrices of the correlations can be saved. Each node is mapped to the biological identifiers given in the layers and the mapping table is returned as annotations.

compute_correlation_matrices(layers, settings)

Arguments

• 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 correlation_matrices and annotations. The second level elements are groupA and groupB (and both at annotations). These contain a named list of matrix objects (correlation_matrices) and data frames (annotations) mapping the graph node IDs to biological identifiers. The third level elements are the layer names given by the user.

Examples

example_settings <- drdimont_settings(
                        handling_missing_data=list(
                            default="all.obs"))

# mini example with reduced mRNA layer for shorter runtime:
data(mrna_data)
reduced_mrna_layer <- make_layer(name="mrna",
                          data_groupA=mrna_data$groupA[1:5,2:6],
                          data_groupB=mrna_data$groupB[1:5,2:6],
                          identifiers_groupA=data.frame(gene_name=mrna_data$groupA$gene_name[1:5]),
                          identifiers_groupB=data.frame(gene_name=mrna_data$groupB$gene_name[1:5]))

example_correlation_matrices <- compute_correlation_matrices(
                                    layers=list(reduced_mrna_layer), 
                                    settings=example_settings)

# to run all layers use layers=layers_example from data(layers_example) 
# in compute_correlation_matrices()