test_validation_rate: Test a network against random

In OceaneCsn/AraNetBench: Evaluates an inferred regulatory network against state of the art interaction databases in Arabidopsis thaliana

Test a network against random

Description

Test a network against random

Usage

test_validation_rate(
  net,
  validation = c("CHIPSeq", "DAPSeq", "TARGET"),
  N = 200
)

Arguments

net	dataframe with a column 'from' (regulators) and a column 'to' (targets), representing the inferred network of edges to evaluate
validation	type of edge in the validation database that should be considered to defined a true/supported prediction in the evaluation process. The validation type must be a vector of one or more of the following values : CHIPSeq, DAPSeq, Litterature, TARGET
N	Number of shuffled network to estimate the null ditribution of the validation rate. 200 by default.

Value

A plot containing the empirical p-pvalue of the permutation-based test

Examples

data("abiotic_stress_Heat_genes_net")
set.seed(999)
results <- evaluate_network(abiotic_stress_Heat_genes_net)
test_validation_rate(abiotic_stress_Heat_genes_net, N = 100)

OceaneCsn/AraNetBench documentation built on May 22, 2022, 12:43 p.m.