weighted.shortest.path: Weighted Shortest Paths compiled between a Gene and...

In vittoriofortino84/ThETA: Transcriptome-based efficacy estimates of target(gene)-disease associations

Description

Determine weighted shortest paths connecting a gene to specified disease-associated genes within relevant tissue-specific PPIs.

Usage

weighted.shortest.path(
  disease_genes,
  ppi_network,
  directed_network = F,
  tissue_expr_data,
  dis_relevant_tissues,
  W,
  cutoff = 1.6,
  verbose = F
)

Arguments

disease_genes	character vector containing the IDs of the genes related to a particular disease. Gene IDs are expected to match with those provided in ppi_network and tissue_expr_data.
ppi_network	a matrix or a data frame with at least two columns reporting the ppi connections (or edges). Each line corresponds to a direct interaction. Columns give the gene IDs of the two interacting proteins.
directed_network	logical indicating whether the PPI is directed.
tissue_expr_data	a numeric matrix or data frame indicating expression significances in the form of Z-scores. Columns are tissues and rows are genes; colnames and rownames must be provided. Gene IDs are expected to match with those provided in ppi_network.
dis_relevant_tissues	a named numeric vector indicating the significances of disease-tissue associations in the form of Z-scores. Names correspond to tissues.
W	a list of discretized Borda-aggregated rankings for each tissue as the one compiled by get_node_centrality.
cutoff	numeric value indicating the cut-off for the disease-associated tissue scores.
verbose	logical indicating whether the messages will be displayed or not in the screen.

Details

This function implements the core of the tissue-specific score described in \insertRefFailli2019ThETA.

Value

A list of two score objects:
- shortest_paths: a shortest path score for each pair <gene, tissue>;
- shortest_paths_avg: average of shortest path scores compiled from all relavant tissues.

vittoriofortino84/ThETA documentation built on May 23, 2021, 4:24 a.m.