random_walk: Random Walk with Restart (RWR)

In AnaGalhoz37/wppi: Weighting protein-protein interactions

Random Walk with Restart (RWR)

Description

RWR on the normalized weighted adjacency matrix. The RWR algorithm estimates each protein/gene relevance based on the functional similarity of genes and disease/phenotype, and the topology of the network. This similarity score between nodes measures how closely two proteins/genes are related in a network. Thus, enabling to identify which candidate genes are more related to our given genes of interest.

Usage

random_walk(weighted_adj_matrix, restart_prob = 0.4, threshold = 1e-05)

Arguments

weighted_adj_matrix	Matrix object corresponding to the weighted adjacency from weighted_adj.
restart_prob	Positive value between 0 and 1 defining the restart probability parameter used in the RWR algorithm. If not specified, 0.4 is the default value.
threshold	Positive value depicting the threshold parameter in the RWR algorithm. When the error between probabilities is smaller than the threshold defined, the algorithm stops. If not specified, 1e-5 is the default value.

Value

Matrix of correlation/probabilities for the functional similarities for all proteins/genes in the network.

See Also

• weighted_adj

• prioritization_genes

• score_candidate_genes_from_PPI

Examples

db <- wppi_data()
GO_data <- db$go
HPO_data <- db$hpo
# Genes of interest
genes_interest <-
    c("ERCC8", "AKT3", "NOL3", "GFI1B", "CDC25A", "TPX2", "SHE")
# Graph object with PPI 
graph_op <- graph_from_op(db$omnipath)
graph_op_1 <- subgraph_op(graph_op, genes_interest, 1)
# Filter ontology data
GO_data_filtered <- filter_annot_with_network(GO_data, graph_op_1)
HPO_data_filtered <- filter_annot_with_network(HPO_data, graph_op_1)
# Weighted adjacency
w_adj <- weighted_adj(graph_op_1, GO_data_filtered, HPO_data_filtered)
# Random Walk with Restart
w_rw <- random_walk(w_adj)

AnaGalhoz37/wppi documentation built on Nov. 8, 2022, 7:47 a.m.