sampleOrder: Order samples within a network.
In InouyeLab/NetRep: Permutation Testing Network Module Preservation Across Datasets

Description Usage Arguments Details Value See Also Examples

Get the order of samples within a module based on the module summary vector.

sampleOrder(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  na.rm = FALSE,
  simplify = TRUE,
  verbose = TRUE
)

`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 `network` for that dataset. The columns should correspond to variables in the data (nodes in the network) and rows to samples in that dataset.
`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 `data` used to infer the interaction network for that 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, 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.
`backgroundLabel`	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.
`discovery`	a vector of names or indices denoting the discovery dataset(s) in the `data`, `correlation`, `network`, `moduleAssignments`, `modules`, and `test` lists.
`test`	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.
`na.rm`	logical; If `TRUE` variables present in the `discovery` dataset but missing from the `test` dataset are excluded. If `FALSE` missing variables are put last in the ordering.
`simplify`	logical; if `TRUE`, simplify the structure of the output list if possible (see Return Value).
`verbose`	logical; should progress be reported? Default is `TRUE`.

Input data structures:

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 sampleOrder 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.

Analysing large datasets:

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.

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 sample ordering are requested for 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 samples calculated in "Dataset2", where "module1" was indentified in "Dataset1". Modules and datasets for which calculation of the sample order have not been requested will contain NULL.

networkProperties

# 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 nodes within module 1 in descending order by module summary
samples <- sampleOrder(
  network=network_list, data=data_list, correlation=correlation_list,
  moduleAssignments=labels_list, modules="1" 
)