monsterNI: Bipartite Edge Reconstruction from Expression data

In QuackenbushLab/MONSTER: Modeling Network State Transitions from Expression and Regulatory data (MONSTER)

Description

This function generates a complete bipartite network from gene expression data and sequence motif data

Usage

monsterNI(motif.data, expr.data, verbose = FALSE, randomize = "none",
  method = "bere", ni.coefficient.cutoff = NA, alphaw = 1,
  regularization = "none", score = "motifincluded", cpp = FALSE)

Arguments

motif.data	A motif dataset, a data.frame, matrix or exprSet containing 3 columns. Each row describes an motif associated with a transcription factor (column 1) a gene (column 2) and a score (column 3) for the motif.
expr.data	An expression dataset, as a genes (rows) by samples (columns)
verbose	logical to indicate printing of output for algorithm progress.
randomize	logical indicating randomization by genes, within genes or none
method	String to indicate algorithm method. Must be one of "bere","pearson","cd","lda", or "wcd". Default is "bere"
ni.coefficient.cutoff	numeric to specify a p-value cutoff at the network inference step. Default is NA, indicating inclusion of all coefficients.
alphaw	A weight parameter specifying proportion of weight to give to indirect compared to direct evidence. See documentation.
regularization	String parameter indicating one of "none", "L1", "L2"
score	String to indicate whether motif information will be readded upon completion of the algorithm
cpp	logical use C++ for maximum speed, set to false if unable to run.

Value

matrix for inferred network between TFs and genes

Examples

data(yeast)
cc.net <- monsterNI(yeast$motif,yeast$exp.cc)

QuackenbushLab/MONSTER documentation built on Oct. 22, 2020, 8:07 a.m.