Description Usage Arguments Details Value References See Also Examples
Order nodes in descending order of weighted degree and order modules by the similarity of their summary vectors.
1 2 3 4 
network 
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. 
data 
a list of matrices, one for each dataset. Each entry of the list
should be the data used to infer the interaction 
correlation 
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

moduleAssignments 
a list of vectors, one for each discovery dataset, containing the module assignments for each node in that dataset. 
modules 
a list of vectors, one for each 
backgroundLabel 
a single label given to nodes that do not belong to
any module in the 
discovery 
a vector of names or indices denoting the discovery
dataset(s) in the 
test 
a list of vectors, one for each 
na.rm 
logical; If 
orderModules 
logical; if 
mean 
logical; if 
simplify 
logical; if 
verbose 
logical; should progress be reported? Default is 
The preservation of network modules in a second
dataset is quantified by measuring the preservation of topological
properties between the discovery and test datasets. These
properties are calculated not only from the interaction networks inferred
in each dataset, but also from the data used to infer those networks (e.g.
gene expression data) as well as the correlation structure between
variables/nodes. Thus, all functions in the NetRep
package have the
following arguments:
network
:
a list of interaction networks, one for each dataset.
data
:
a list of data matrices used to infer those networks, one for each
dataset.
correlation
:
a list of matrices containing the pairwise correlation coefficients
between variables/nodes in each dataset.
moduleAssignments
:
a list of vectors, one for each discovery dataset, containing
the module assignments for each node in that dataset.
modules
:
a list of vectors, one for each discovery dataset, containing
the names of the modules from that dataset to analyse.
discovery
:
a vector indicating the names or indices of the previous arguments'
lists to use as the discovery dataset(s) for the analyses.
test
:
a list of vectors, one vector for each discovery dataset,
containing the names or indices of the network
, data
, and
correlation
argument lists to use as the test dataset(s)
for the analysis of each discovery dataset.
The formatting of these arguments is not strict: each function will attempt
to make sense of the user input. For example, if there is only one
discovery
dataset, then input to the moduleAssigments
and
test
arguments may be vectors, rather than lists. If the
nodeOrder
are being calculate within the discovery or
test datasets, then the discovery
and test
arguments do
not need to be specified, and the input matrices for the network
,
data
, and correlation
arguments do not need to be wrapped in
a list.
Matrices in the network
, data
, and correlation
lists
can be supplied as disk.matrix
objects. This class allows
matrix data to be kept on disk and loaded as required by NetRep.
This dramatically decreases memory usage: the matrices for only one
dataset will be kept in RAM at any point in time.
When multiple 'test'
datasets are specified and 'mean'
is
TRUE
, then the order of nodes will be determine by the average of
each node's weighted degree across datasets. The weighted degree in each
dataset is scaled to the node with the maximum weighted degree in that
module in that dataset: this prevents differences in average edge weight
across datasets from influencing the outcome (otherwise the mean would be
weighted by the overall density of connections in the module). Thus, the
mean weighted degree is a robust measure of a node's relative importance
to a module across datasets. The mean is calculated with
'na.rm=TRUE'
: where a node is missing it does not contribute to
the mean.
A nested list structure. At the top level, the list has one element per
'discovery'
dataset. Each of these elements is a list that has one
element per 'test'
dataset analysed for that 'discovery'
dataset. Each of these elements is a list that has one element per
'modules'
specified, containing a vector of node names for the
requested module. When simplify = TRUE
then the simplest possible
structure will be returned. E.g. if the node ordering are requested for
module(s) in only one dataset, then a single vector of node labels will
be returned.
When simplify = FALSE
then a nested list of datasets will always be
returned, i.e. each element at the top level and second level correspond to
a dataset, and each element at the third level will correspond to modules
discovered in the dataset specified at the top level if module labels are
provided in the corresponding moduleAssignments
list element. E.g.
results[["Dataset1"]][["Dataset2"]][["module1"]]
will contain the
order of nodes calculated in "Dataset2", where "module1" was indentified in
"Dataset1". Modules and datasets for which calculation of the node order
have not been requested will contain NULL
.
Langfelder, P., Mischel, P. S. & Horvath, S. When is hub gene selection better than standard metaanalysis? PLoS One 8, e61505 (2013).
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  # load in example data, correlation, and network matrices for a discovery
# and test dataset:
data("NetRep")
# Set up input lists for each input matrix type across datasets. The list
# elements can have any names, so long as they are consistent between the
# inputs.
network_list < list(discovery=discovery_network, test=test_network)
data_list < list(discovery=discovery_data, test=test_data)
correlation_list < list(discovery=discovery_correlation, test=test_correlation)
labels_list < list(discovery=module_labels)
# Sort modules by similarity and nodes within each module by their weighted
# degree
nodes < nodeOrder(
network=network_list, data=data_list, correlation=correlation_list,
moduleAssignments=labels_list
)

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