ppvpercents: Plotting function showing positive predictive values.

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

Description

Given a set of knonw disease-drug-target(gene) associations and efficacy estimates compiled for diseas-gene associations, it determines an visualize the positive predictive values (ppv). PPVs are computed as the percentage of true positives normalized against the expected ppv for a random ordering of disease-gene pairs.

Usage

ppvpercents(
  gold_std,
  test,
  entrez.col = NULL,
  disease.col = NULL,
  score.col = NULL,
  a.disease = NULL,
  plot = TRUE
)

Arguments

gold_std	a dataframe representing known drug target disease associations. It must contain at least two columns indicating the gene-entrez-id and disease-efo-id (EFO-ID). Ex: target.entrez disease.id description 7039 EFO:0003884 chronic kidney disease 1490 EFO:0009549 female reproductive system disease
test	a dataframe containing efficacy estimates of disease-drug-target(gene) associations. It must contain at least three columns indicating the gene-entrez-id, disease-efo-id (EFO-ID) and the efficacy scores. Ex: target.entrez disease.id modscore 5376 EFO:0000270 0.4614037 28996 EFO:0000270 0.5404949 7403 EFO:0000270 0.6425815
entrez.col	a character value indicating the column for the gene-entrez-id.
disease.col	a character value indicating the column for the disease-efo-id.
score.col	a character value indicating the column for the efficacy estimates.
a.disease	a character value to plot the ppv values for a given disease.
plot	a boolean value to plot the ppv curves.

Details

This function was used for the comparison of different efficacy scoring methods described in \insertRefFailli2019ThETA.

Value

a ggplot showing curves of positive predictive values (PPVs).

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