netbenchmark (version 1.4.2)

noise.bench: Noise sensitivity test

Description

For a given vector of character of the names of wrapper functions that compute a network inference methods, noise.bench performs a noise sensitivity test. It makes use of different big gene datasets adding Gaussian noise with different intensity to evaluate the performance of the methods.

Usage

noise.bench(methods = "all.fast", datasources.names = "all",
        eval = "AUPR", no.topedges = 20,experiments=150,
        datasets.num = 3, local.noise = seq(0, 100, len = 3),
        global.noise = 0, noiseType = "normal", sym = TRUE,
        seed = NULL, verbose = TRUE)

Arguments

methods

A vector of characters containing the names of network inference algorithms wrappers to be compared (default: "all.fast").

datasources.names

A vector of characters containing the names of network datasets to be included in the benchmark (default: "all").

eval

The name of the evaluation metric among the following ones: "no.truepos", "AUROC" or "AUPR" (default : "AUPR") - see evaluate.

experiments

Integer specifying the number of experiments to generate the subsampled datasets (default: 150) - see datasource.subsample.

datasets.num

Number of repetitions in the noise evaluation, for each method and each dataset and each noise intensity (default: 5).

no.topedges

Float specifying the percentage number of links to be considered in the evaluation (default: 20).

local.noise

Vector specifying the desired percentage of local noise to be added at each of the subsampled datasets (default: seq(0, 100, len = 3)).

global.noise

Vector specifying the desired percentage of global noise to be added at each of the subsampled datasets (default: 0).

noiseType

Character specifying the type of the noise to be added: "normal" (default: "normal").

sym

Logical specifying if the evaluation is symmetric (default: TRUE) - see evaluate.

seed

A single value, interpreted as an integer to specify seeds, useful for creating simulations that can be reproduced (default: NULL) - see set.seed.

verbose

Logical specifying if the code should provide a log about what the function is doing (default: TRUE).

Value

• noise.bench returns a list with three elements:
  1. A data.frame which is the result table containing the number of true positives as an evaluation measure. It evaluates each algorithm specified atmethodsat each one of the specifieddatasources.nameswith thelocal.noiseandglobal.noisespecified. For each combination the algorithms are evaluateddatasets.numtimes and their results are averaged.
  2. A data.frame which is the corresponding pvalue table of the corresponding statistical test for each one of thedatasets.numbetween the best algorithm and the others.
  3. The seed of the random number generators that allows the replication of the results.

Details

The argument methods accepts "all.fast" and "all" (case insensitive) as a parameters:

• "all.fast" performs network inference with "aracne", "c3net", "clr", "GeneNet", "mutual ranking", "mrnetb", "pcit"
• "all" performs network inference with "aracne", "c3net", "clr", "GeneNet", "Genie3", "mutual ranking", "mrnet", "mrnetb", "pcit"

It evaluates the first no.topedges % of the possible links inferred by each algorithm at each dataset.

See Also

netbenchmark, experiments.bench

Examples

results <- noise.bench(datasources.names="toy",
        datasets.num=2,methods="all.fast",experiments=NULL)