netbenchmark.data: Benchmarking of several network inference algorithms for your...
In paubellot/netbenchmark: Benchmarking of several gene network inference methods

Description Usage Arguments Details Value Author(s) See Also Examples

Benchmarking of several network inference algorithms for your own data

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netbenchmark.data(methods = "all.fast", data = NULL, true.net = NULL,
  eval = "AUPR", no.topedges = 20, sym = TRUE, plot = FALSE,
  verbose = TRUE)

`methods`	A vector of characters containing the names of network i inference algorithms wrappers to be compared (default: "all.fast").
`data`	data.frame containing the data. Each row should contain a microarray experiment and each column a gene (default: NULL).
`true.net`	matrix containg underlying network in the form of adjacency matrix (default: NULL).
`eval`	The name of the evaluation metric among the following ones: "no.truepos", "AUROC" or "AUPR" (default : "AUPR").
`no.topedges`	Float specifying the percentage number of links to be considered in the evaluation (default: 20)
`sym`	Logical specifying if the evaluation is symmetric (default: TRUE) - see `evaluate`
`plot`	(default: FALSE)
`verbose`	Logical specifying if the code should provide a log about what the function is doing (default: TRUE).

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", "mrnet" (and registered methods with RegisterWrapper.)
"all" performs network inference with "aracne", "c3net", "clr", "GeneNet", "Genie3", "mutual ranking", "mrnet", "mrnetb" (and registered methods with RegisterWrapper.)

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

netbenchmark.data returns a list with three elements.

A data.frame which is the result table of the selected measure.
A data.frame which contains the CPU Time Used (in seconds) by the algorithm to infer the network.
A list containing the mean precision recall curves of the different algorithms for each datasource.

Each of these data.frame will have the same number of columns as methods provided by the user and an additional one for a random method.

Pau Bellot, Catharina Olsen and Patrick E Meyer Maintainer: Pau Bellot <pau.bellot@upc.edu>

netbenchmark, evaluate, comp.metr

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    Data <- grndata::getData(datasource.name="toy")
    top20.aupr <- netbenchmark.data(methods="all",data = Data[[1]],
                               true.net= Data[[2]])