compute_correlation_matrices {DrDimont} | R Documentation |
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)
Arguments
layers |
[list] Named list with different network layers containing data and identifiers for both
groups (generated from |
settings |
[list] A named list containing pipeline settings. The settings list has to be
initialized by |
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.
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()