visualize.graph: Interactive visualization of tissue-specific networks.

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

Description

It uses visNetwork to build a neteworks showing all short pathways connecting disease genes with putative drug targets.

Usage

visualize.graph(
  tissue_scores,
  disease_genes,
  ppi_network,
  directed_network = FALSE,
  tissue_expr_data,
  top_targets = NULL,
  orgdb_go = "org.Hs.eg.db",
  db = "kegg",
  verbose = FALSE
)

Arguments

tissue_scores	a data.frame as the one compiled by get.tissue.specific.scores
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.
top_targets	character vector indicating a list of ENTREZ id to be used for the slection of the shortest paths.
orgdb_go	a character specifying the organism for GO. Deafault value is org.Hs.eg.db.
db	character indicating the database to consider for enrichment analysis. Possible values are: kegg, BP, MF and CC. Defaults to kegg.
verbose	logical indicating whether the messages will be displayed or not in the screen.

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