DrDimont (version 0.1.4)

compute_correlation_matrices: Computes correlation matrices for specified network layers

Description

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`.

Usage

compute_correlation_matrices(layers, settings)

Value

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.

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.

Examples

# \dontshow{
WGCNA::disableWGCNAThreads()
# }

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()