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

In dschlauch/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

bereFull(motifs, exprData, alpha = 0.5, penalized = TRUE, lambda = 10,
  score = "motifincluded")

Arguments

alpha	A weight parameter specifying proportion of weight to give to indirect compared to direct evidence. See documentation.
score	String to indicate whether motif information will be readded upon completion of the algorithm
motif	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	An expression dataset, as a genes (rows) by samples (columns) data.frame
verbose	logical to indicate printing of output for algorithm progress.
method	String to indicate algorithm method. Must be one of "cd","lda", or "wcd". Default is correlation difference "cd".

Value

TBD, An object of class "bere" (currently matrix or data.frame)

Examples

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dschlauch/MONSTER documentation built on May 15, 2019, 2:57 p.m.