This function generates a complete bipartite network from gene expression data and sequence motif data
Usage
monsterMonsterNI(
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". Important note: the direct regulatory network observed from gene expression is currently implemented as a regular correlation as opposed to the partial correlation described in the paper (please see Schlauch et al., 2017, https://doi.org/10.1186/s12918-017-0517-y)
- 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 between 0 and 1 specifying proportion of weight to give to indirect compared to direct evidence. The default is 0.5 to give an equal weight to direct and indirect evidence.
- regularization
String parameter indicating one of "none", "L1", "L2"
- score
String to indicate whether motif information will be readded upon completion of the algorithm to give to indirect compared to direct evidence. See documentation.
- cpp
logical use C++ for maximum speed, set to false if unable to run.
Examples
data(yeast)
cc.net <- monsterMonsterNI(yeast$motif,yeast$exp.cc)