generateNetwork: Network inference with Gaussian graphical models (GGMs) or...

Description Usage Arguments Value References Examples

View source: R/generateNetwork.R

Description

Network inference with Gaussian graphical models (GGMs) or Pearson correlation

Usage

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generateNetwork(data, covars = NULL, annotations,
  correlation.type = "partial", alpha = 0.05,
  correction.method = "bonferroni")

Arguments

data

a data.table or matrix containing the data. The columns correspond to the variables (e.g. metabolites), the rows to the observations.

covars

a data.table containing covariates to correct for. The columns correspond to the different covariates, the rows to the observations.

annotations

a data.table containing annotations for the variables (e.g. pathway annotations). The columns correspond to the different annotations, the rows to the variables

correlation.type

type of correlation to be estimated. Can either be "pearson", or "partial".

alpha

significance level (type 1 error) for multiple testing correction.

correction.method

the method that should be used for multiple testing correction ("bonferroni", "BH", "BY", "fdr", "holm", "hochberg", "hommel", "none"). Default is bonferroni. See p.adjust.

Value

a network containing the variables as nodes as an igraph object.

References

\insertRef

Krumsiek2011MoDentify

Examples

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data(qmdiab.data)
data(qmdiab.annos)

net.graph <- generateNetwork(
  data = qmdiab.data, annotations = qmdiab.annos,
  alpha = 0.05, correction.method = "bonferroni"
)

krumsieklab/MoDentify documentation built on March 24, 2021, 9:01 p.m.