Template parameters to be imported into other function documentation. This is not intended to be a stand-alone help file.
a list of interaction networks, one for each dataset. Each entry of the list should be a \(n * n\) matrix or where each element contains the edge weight between nodes \(i\) and \(j\) in the inferred network for that dataset.
a list of matrices, one for each dataset. Each entry of the list
should be the data used to infer the interaction network for that
dataset. The columns should correspond to variables in the data
(nodes in the network) and rows to samples in that dataset.
a list of matrices, one for each dataset. Each entry of
the list should be a \(n * n\) matrix where each element contains the
correlation coefficient between nodes \(i\) and \(j\) in the
data used to infer the interaction network for that dataset.
a list of vectors, one for each discovery dataset, containing the module assignments for each node in that dataset.
a list of vectors, one for each discovery dataset,
of modules to perform the analysis on. If unspecified, all modules
in each discovery dataset will be analysed, with the exception of
those specified in backgroundLabel argument.
a single label given to nodes that do not belong to
any module in the moduleAssignments argument. Defaults to "0". Set
to NULL if you do not want to skip the network background module.
a vector of names or indices denoting the discovery
dataset(s) in the data, correlation, network,
moduleAssignments, modules, and test lists.
a list of vectors, one for each discovery dataset,
of names or indices denoting the test dataset(s) in the data,
correlation, and network lists.
logical; should progress be reported? Default is TRUE.