weighted_adj: Weighted adjacency matrix

In AnaGalhoz37/wppi: Weighting protein-protein interactions

Weighted adjacency matrix

Description

Converts adjacency to weighted adjacency using network topology information (shared neighbors between connected nodes via common_neighbors) integrated with genome and phenotype factors from GO and HPO annotation terms (functionality computed by functional_annot). At the end, the weighted adjacency matrix is normalized by column.

Usage

weighted_adj(graph_op, GO_data, HPO_data)

Arguments

graph_op	Igraph object based on OmniPath PPI interactions from graph_from_op.
GO_data	Data frame with GO annotations as provided by wppi_go_data.
HPO_data	Data frame with HPO annotations as provided by wppi_hpo_data.

Value

Weighted adjacency matrix based on network topology and functional similarity between interacting proteins/genes based on ontology databases.

See Also

• random_walk

• prioritization_genes

• common_neighbors

• graph_from_op

• subgraph_op

• score_candidate_genes_from_PPI

• wppi_go_data

• wppi_hpo_data

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)

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