InfoWalkR

package implementation of the MultiOme Paper

library(InfoWalkR, quietly = TRUE)

Data Import

#read in data
path = '<path_to_your_networks>'
graph_list = read_graphs_from_folder(path)

graph_list

All edgelists are structured like this:

# Graph Type: undirected
A	B
A1BG	ABCC4
A1BG	ALB
A1BG	APLP2
A1BG	BRPF3

The Graph type flag is needed to determine whether the graph is directed or not. When a third column is present that specifies edge weight, the graph is read as a weighted graph. As long as the node type is the same in the multiplex, different edge types are allowed across different layers.

#test gene set
path_gene_set = '<path_to_your_test_gene_set>'

# Read the TSV file
data <- read.delim(path_gene_set, sep = "\t", header = TRUE)

# Extract the gene names column as a vector
# in the example, th e
gene_set <- unlist(strsplit(data$all_genes, ";"))

gene_set

Layer Selection

# perform layer selection based on lcc that seed genes form
#alpha sets the threshold for significance
lcc_results = lcc_based_network_selection(graph_list, gene_set,
                                          trial = 100, randomization = 'random',
                                          alpha = 0.05)
#pull results
lcc_results$results

layer_weights = lcc_results$results
sig_layers = lcc_results$significant_networks

The lcc_results object contains:

1. the dataframe containing only the significant layers and the associated z_score --> this is for the layer weights (weighted_layer_df)
2. the graph_list containing only the significant layers

Random Walk

Parameter	Description
rank_aggregate	If TRUE, aggregates RWR ranking results across all layers and returns summary statistics. If FALSE, this step is skipped.
rank_individual	If TRUE, ranks RWR results within each layer and returns results for all layers individually. If FALSE, this step is skipped.
remove_seeds	If TRUE, removes seed genes from ranking. If FALSE, seed genes are retained in the ranking process.

If both rank_aggregata = FALSE and rank_individual = FALSE, the function returns the raw results for each individual layer

#perform random walk with restart on multiplex network
#the default is equal weights on the seed gene set

infowalk_results = weighted_multiplex_propagation(layer_weights_df = layer_weights,
                                                    graph_list = sig_layers,
                                                    seed_set = gene_set,
                                                    rank_aggregate = TRUE,
                                                    rank_individual = FALSE,
                                                    remove_seeds = FALSE)
head(infowalk_results)

Description of summary statistics

Output	Description
mean_arithmetic	The arithmetic mean of visiting probabilities of a given node (gene-wise average across all layers).
mean_geometric	Geometric mean of visiting probabilities of a given node (gene-wise geometric mean across all layers).
rank_all_geometric	Expresses the visiting probabilities of a node across all layers (aggregated by geometric mean) as a rank. The calculation ranks the visiting probabilities of a node across all layers and then aggregates the ranks by geometric mean.
rank_each_geometric	Expresses the visiting probabilities of a node across all layers as a rank, but the calculation first ranks the visiting probabilities for the nodes within each layer and then aggregates the result by geometric mean.
rank_mean_arithmetic	Expresses the aggregated visiting probabilities for each node across all layers average as rank.
rank_mean_geometric	Expresses the aggregated visiting probabilities for each node across all layers Geometric Avg as rank.

Results for individual layers

infowalk_results = weighted_multiplex_propagation(layer_weights_df = layer_weights,
                                                    graph_list = sig_layers,
                                                    seed_set = gene_set,
                                                    rank_aggregate = FALSE,
                                                    rank_individual = TRUE,
                                                    remove_seeds = FALSE)
head(infowalk_results)

Visualizations

#plot degree distribution
plot_degree_dist(graph = sig_layers[[1]])

#plot z-score ranking of layers
plot_z_score_ranking(results_df = lcc_results$results, alpha= 0.00001)

#plot histograms of lcc sizes
plot_lcc_overview(results_df = lcc_results$results)

pre-calculate the supra-adjacency matrix and then perform weighted multiplex propagation

The calculation of the normalized supra-adjacency matrix takes the most time, therefore pre-computing the supra_adjacecncy matrix might be preferable.

supra_adjacency_matrix = construct_supraadjacency_matrix(layer_weights, sig_layers)

infowalk_results = weighted_multiplex_propagation(supra_adjacency_matrix = supra_adjacency_matrix,
                                                    layer_weights_df = layer_weights,
                                                    graph_list = sig_layers,
                                                    seed_set = gene_set,
                                                    rank_aggregate = TRUE,
                                                    rank_individual = FALSE,
                                                    remove_seeds = FALSE)