build.tissue.specific.networks: Compile tissue-specific networks and, within these, set of...

Description Usage Arguments Details Value

View source: R/script_tissue_specific_efficacy_score.R

Description

Given a set of gene targets, it builds the corresponding tissue-specific networks, the set of genes linking the targets to disease genes, a set of genes tightly connected to all targets.

Usage

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build.tissue.specific.networks(
  tissue_scores,
  disease_genes,
  ppi_network,
  directed_network = F,
  tissue_expr_data,
  top_targets = NULL,
  rwr_restart = 0.75,
  rwr_norm = "quantile",
  rwr_cutoff = 0.001,
  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.

rwr_restart

the restart probability used for RWR. See dnet::dRWR for more details.

rwr_norm

the way to normalise the adjacency matrix of the input graph. See dnet::dRWR for more details.

rwr_cutoff

the cuoff value to select the most visited genes.

verbose

logical indicating whether the messages will be displayed or not in the screen.

Details

The top targets are used to re-build the shortest paths with the disease-relevant genes in tissue-specific networks. The shortest paths linkining a top target to disease genes are merged and the resulting set of nodes/genes are giving in output. Moreover, random walk with restart is utilized to identify a set of genes that is tightly connected to the targets.

Value

a list of four objects:
- tsn: a list of tissue-specific networks;
- shp: a list of gene sets, each gene set indicates the genes connecting a target to all disease genes;
- tsn: a list of gene sets, each gene set represents the set of genese that are closely related to the set of targets;
- universe: the total number of genes in the tissue-specific networks.


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