buildExpNet: Build experimental networks

In MetClassNet/MetClassNetR: Create and analize networks built from metabolomics Data

Build experimental networks

Description

The function buildExpNet builds experimental networks, based on spectral similarity, correlation, and mass difference

Usage

buildExpNet(
  inputData,
  net2Build = "all",
  directed = FALSE,
  ppmMass = 10,
  ppmSpec = 0,
  tol = 0.005,
  corrModel = "pearson",
  corrThresh = 0.25,
  specSimThresh = 0.7
)

Arguments

inputData	list, list returned by the loadInputData function
net2Build	list, list of experimental networks to build, according to the following options: "all" - generates 3 experimental networks: mass difference, correlation, and spectral similarity, "m" - builds the mass difference network, "s" - builds the spectral similarity network, "c" - builds the correlation network. It is to note that combinations of m, s, and c are possible, writing them separated by commas, e.g., c("m", "c") would generate the mass difference, and correlation networks. The default value is "all"
directed	boolean, boolean value (TRUE/FALSE). If TRUE then the networks that are generated will be directed, and undirected otherwise. The default value is FALSE (i.e., undirected network)
ppmMass	numeric, (optional) allowed error for mass differences calculus. It is only needed if the mass difference network is to be built. The default value is 10
ppmSpec	numeric, (optional) relative allowed error for spectral similarity calculus. It is only needed if the spectral similarity network is to be built. The default value is 0
tol	numeric, (optional) absolute tolerance for spectral similarity calculus. It is only needed if the spectral similarity network is to be built. The default value is 0.005
corrModel	vector, (optional) character vector containing the model(s) to be used for the correlation calculus in MetNet. Please check the available models in the current version of MetNet. There are 10 models available in version 1.14.0: "lasso", "randomForest", "clr", "aracne", "pearson", "pearson_partial", "spearman", "spearman_partial", "ggm", and "bayes". This parameter is only needed if the correlation network is to be built. The default value is "pearson"
corrThresh	numeric, (optional) floating point number indicating the correlation threshold to consider that two features are correlated. It is only needed if the correlation network is to be built. The default value is 0.25 (i.e., at least 25% of correlation between the abundance values, either positive or negative)
specSimThresh	numeric, (optional) floating point number indicating the spectral similarity threshold to consider that two features have similar spectra. It is only needed if the spectral similarity network is to be built. The default value is 0.7

Value

List of experimental networks as igraph objects

Author(s)

Elva Novoa, [e-mail]

Examples

# See the MultiLayerNetwork vignette

MetClassNet/MetClassNetR documentation built on June 30, 2023, 2:12 p.m.