bereFull: Bipartite Edge Reconstruction from Expression data (composite...

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. This NI method serves as a default method for inferring bipartite networks in MONSTER. Running bereFull can generate these networks independently from the larger MONSTER method.

Usage

bereFull(motif.data, expr.data, alpha = 0.5, lambda = 10,
  score = "motifincluded")

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) data.frame
alpha	A weight parameter specifying proportion of weight to give to indirect compared to direct evidence. See documentation.
lambda	if using penalized, the lambda parameter in the penalized logistic regression
score	String to indicate whether motif information will be readded upon completion of the algorithm

Value

An matrix or data.frame

Examples

data(yeast)
monsterRes <- bereFull(yeast$motif, yeast$exp.cc, alpha=.5)

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