tissue.specific.scores: Compile tissue-specific scores for a given disease.

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

Description

Determine the tissue-specific scores for genes connected to disease-associated genes within relevant tissue-specific PPIs.

Usage

tissue.specific.scores(
  disease_genes,
  ppi_network,
  directed_network = F,
  tissue_expr_data,
  dis_relevant_tissues,
  W,
  cutoff = 1.6,
  verbose = FALSE
)

list.tissue.specific.scores(
  disease_gene_list,
  ppi_network,
  directed_network = F,
  tissue_expr_data,
  dis_relevant_tissues,
  W,
  cutoff = 1.6,
  verbose = FALSE,
  parallel = NULL
)

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 in case of tissue.specific.scores or a numeric matrix in case of list.tissue.specific.scores indicating the significances of disease-tissue associations in the form of Z-scores. Vector names correspond to tissue; matrix colnames and rownames correspond to tissues and diseases, respectively. Names must be provided.
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.
disease_gene_list	a list of disease-associated genes. Each element of the list is a character vector containing the IDs of the genes related to a particular disease.
parallel	an integer indicating how many cores will be registered for parallel computation.

Details

This function implements the tissue-specific efficacy estimates of target-disease associations described in \insertRefFailli2019ThETA. This function use weighted.shortest.path to compile weighted shortest paths connecting a gene to specified disease-associated genes within relevant tissue-specific PPIs. The interquartile range (IQR) is applied to remove outliers and the final set of efficacy scores are rescaled between 1 and 0, with 1 indicating the most effective targets.

Value

a data frame or a list of data frames containing tissue specific scores.

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