Fast permutation procedure for testing network module replication

Description

Functions for assessing the replication/preservation of a network module's topology across datasets through permutation testing. This is suitable for networks that can be meaningfully inferred from multiple datasets. These include gene coexpression networks, protein-protein interaction networks, and microbial interaction networks. Modules within these networks consist of groups of nodes that are particularly interesting: for example a group of tightly connected genes associated with a disease, groups of genes annotated with the same term in the Gene Ontology database, or groups of interacting microbial species, i.e. communities. Application of this method can answer questions such as; (1) do the relationships between genes in a module replicate in an independent cohort? (2) are these gene coexpression modules preserved across tissues or tissue specific? (3) are these modules conserved across species? (4) are microbial communities preserved across multiple spatial locations?

Details

The main function for this package is modulePreservation. Several functions for downstream are also provided: networkProperties for calculating the topological properties of a module, and plotModule for visualising a module.

Author(s)

Maintainer: Scott Ritchie sritchie73@gmail.com (0000-0002-8454-9548)

See Also

Useful links:

• Report bugs at https://github.com/sritchie73/NetRep/issues

Combine results of multiple permutation procedures

Description

This function takes the output from multiple runs of modulePreservation, combines their results, and returns a new set of permutation test P-values. This is useful for parallelising calculations across multiple machines.

Usage

combineAnalyses(pres1, pres2)

Arguments

Details

The calls to 'modulePreservation' must have been identical for both input lists, with the exception of the number of threads used and the number of permutations calculated.

Value

A nested list containing the same elements as modulePreservation.

Examples

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)

pres1 <- modulePreservation(
 network=network_list, data=data_list, correlation=correlation_list,
 moduleAssignments=labels_list, nPerm=1000, discovery="discovery", 
 test="test", nThreads=2
)

pres2 <- modulePreservation(
 network=network_list, data=data_list, correlation=correlation_list, 
 moduleAssignments=labels_list, nPerm=1000, discovery="discovery", 
 test="test", nThreads=2
)

combined <- combineAnalyses(pres1, pres2)

Template parameters

Description

Template parameters to be imported into other function documentation. This is not intended to be a stand-alone help file.

Arguments

The 'disk.matrix' class

Description

A 'disk.matrix' contains a file path to a matrix stored on disk, along with meta data for how to read that file. This allows NetRep to load datasets into RAM only when required, i.e. one at a time. This significantly reduces the memory usage of R when analysing large datasets. 'disk.matrix' objects may be supplied instead of 'matrix' objects in the input list arguments 'network', 'data', and 'correlation', which are common to most of NetRep's functions.

Usage

attach.disk.matrix(file, serialized = TRUE, ...)

serialize.table(file, ...)

is.disk.matrix(x)

as.disk.matrix(x, file, serialize = TRUE)

## S4 method for signature 'disk.matrix'
as.disk.matrix(x, file, serialize = TRUE)

## S4 method for signature 'matrix'
as.disk.matrix(x, file, serialize = TRUE)

## S4 method for signature 'ANY'
as.disk.matrix(x, file, serialize = TRUE)

## S4 method for signature 'disk.matrix'
as.matrix(x)

## S4 method for signature 'disk.matrix'
show(object)

Arguments

Details

Matrices may either be stored as regular table files that can be read by read.table, or as serialized R objects that can be read by readRDS. Serialized objects are much faster to load, but cannot be read by other programs.

The attach.disk.matrix function creates a disk.matrix object from a file path. The as.matrix function will load the data from disk into the R session as a regular matrix object.

The as.disk.matrix function converts a matrix into a disk.matrix by saving its contents to the specified file. The serialize argument determines whether the data is stored as a serialized R object or as a tab-separated file (i.e. sep="\t"). We recommend storing the matrix as a serialized R object unless disk space is a concern. More control over the storage format can be obtained by using saveRDS or write.table directly.

The serialize.matrix function converts a file in table format to a serialized R object with the same file name, but with the ".rds" extension.

Value

A disk.matrix object (attach.disk.matrix, as.disk.matrix), a matrix (as.matrix), the file path to a serialized matrix (serialize.table), or a TRUE or FALSE indicating whether an object is a disk.matrix (is.disk.matrix).

Slots

file

the name of the file where the matrix is saved.

read.func

either "read.table" or "readRDS".

func.args

a list of arguments to be supplied to the 'read.func'.

Warning

attach.disk.matrix does not check whether the specified file can be read into R. as.matrix will fail and throw an error if this is the case.

Example data

Description

Example gene coexpression networks inferred from two independent datasets to demonstrate the usage of package functions.

Usage

data("NetRep")

Format

"discovery_network"

a matrix with 150 columns and 150 rows containing the network edge weights encoding the interaction strength between each pair of genes in the discovery dataset.

"discovery_data"

a matrix with 150 columns (genes) and 30 rows (samples) whose entries correspond to the expression level of each gene in each sample in the discovery dataset.

"discovery_correlation"

a matrix with 150 columns and 150 rows containing the correlation-coefficients between each pair of genes calculated from the "discovery_data" matrix.

\"module_labels"

a named vector with 150 entries containing the module assignment for each gene as identified in the discovery dataset.

"test_network"

a matrix with 150 columns and 150 rows containing the network edge weights encoding the interaction strength between each pair of genes in the test dataset.

"test_data"

a matrix with 150 columns (genes) and 30 rows (samples) whose entries correspond to the expression level of each gene in each sample in the test dataset.

"test_correlation"

a matrix with 150 columns and 150 rows containing the correlation-coefficients between each pair of genes calculated from the "test_data" matrix.

An object of class matrix (inherits from array) with 150 rows and 150 columns.

An object of class matrix (inherits from array) with 30 rows and 150 columns.

An object of class matrix (inherits from array) with 150 rows and 150 columns.

An object of class numeric of length 150.

An object of class matrix (inherits from array) with 150 rows and 150 columns.

An object of class matrix (inherits from array) with 30 rows and 150 columns.

An object of class matrix (inherits from array) with 150 rows and 150 columns.

Details

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 vector 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.

This data is used to provide concrete examples of the usage of these arguments in each package function.

Simulation details

The discovery gene expression dataset ("discovery_data") containing 30 samples and 150 genes was simulated to contain four distinct modules of sizes 20, 25, 30, and 35 genes. Data for each module were simulated as:

G^{(w)}_{simulated} = E^{(w)} r_i + \sqrt{1 - r^2_i} \epsilon

Where E^{(w)} is the simulated module's summary vector, r is the simulated module's node contributions for each gene, and \epsilon is the error term drawn from a standard normal distribution. E^{(w)} and r were simulated by bootstrapping (sampling with replacement) samples and genes from the corresponding vectors in modules 63, 51, 57, and 50 discovered in the liver tissue gene expression data from a publicly available mouse dataset (see reference (1) for details on the dataset and network discovery). The remaining 40 genes that were not part of any module were simulated by randomly selecting 40 liver genes and bootstrapping 30 samples and adding the noise term, \epsilon. A vector of module assignments was created ("module_labels") in which each gene was labelled with a number 1-4 corresponding to the module they were simulated to be coexpressed with, or a label of 0 for the for the 40 "background" genes not participating in any module. The correlation structure ("discovery_correlation") was calculated as the Pearson's correlation coefficient between genes (cor(discovery_data)). Edge weights in the interaction network ("discovery_network") were calculated as the absolute value of the correlation coefficient exponentiated to the power 5 (abs(discovery_correlation)^5).

An independent test dataset ("test_data") containing the same 150 genes as the discovery dataset but 30 different samples was simulated as above. Modules 1 and 4 (containing 20 and 35 genes respectively) were simulated to be preserved using the same equation above, where the summary vector E^{(w)} was bootstrapped from the same liver modules (modules 63 and 50) as in the discovery and with identical node contributions r as in the discovery dataset. Genes in modules 2 and 3 were simulated as "background" genes, i.e. not preserved as described above. The correlation structure between genes in the test dataset ("test_correlation") and the interaction network ("test_network") were calculated the same way as in the discovery dataset.

The random seed used for the simulations was 37.

References

1. Ritchie, S.C., et al., A scalable permutation approach reveals replication and preservation patterns of network modules in large datasets. Cell Systems. 3, 71-82 (2016).

See Also

modulePreservation, plotModule, and networkProperties.

Load a 'bigMatrix' (deprecated)

Description

The 'bigMatrix' class is no longer implemented in the NetRep package: the shared memory approach was incompatabile with high performance compute clusters, so the parallel permutation procedure has been translated into C++ code (which is also much faster). The disk.matrix class should now be used instead when analysing large datasets.

Usage

load.bigMatrix(backingfile)

Arguments

Details

This function will convert 'bigMatrix' data saved by previous versions of NetRep to a serialized R matrix saved in the same location and return a disk.matrix object with the associated file path. If this conversion has taken place already the function will throw a warning.

This function will also convert the 'bigMatrix' descriptor file to a big.matrix descriptor file to preserve compatability with functions in the bigmemory package. If this functionality is not required, the files with the extensions ".bin" and ".desc" may be removed.

A note for users using multi-node high performance clusters: 'big.matrix' objects are not suitable for general usage. Access to file-backed shared memory segments on multi-node systems is very slow due to consistency checks performed by the operating system. This becomes exponentially worse the more R sessions there are simultaneously accessing the shared memory segment, e.g. through parallel foreach loops.

Replication and preservation of network modules across datasets

Description

Quantify the preservation of network modules (sub-graphs) in an independent dataset through permutation testing on module topology. Seven network statistics (see details) are calculated for each module and then tested by comparing to distributions generated from their calculation on random subsets in the test dataset.

Usage

modulePreservation(
  network,
  data,
  correlation,
  moduleAssignments,
  modules = NULL,
  backgroundLabel = "0",
  discovery = 1,
  test = 2,
  selfPreservation = FALSE,
  nThreads = NULL,
  nPerm = NULL,
  null = "overlap",
  alternative = "greater",
  simplify = TRUE,
  verbose = TRUE
)

Arguments

Details

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.

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.

Additional memory usage of the permutation procedure is directly proportional to the sum of module sizes squared multiplied by the number of threads. Very large modules may result in significant additional memory usage per core due to extraction of the correlation coefficient sub-matrix at each permutation.

Module Preservation Statistics:

Module preservation is assessed through seven module preservation statistics, each of which captures a different aspect of a module's topology; i.e. the structure of the relationships between its nodes (1,2). Below is a description of each statistic, what they seek to measure, and where their interpretation may be inappropriate.

The module coherence ('coherence'), average node contribution ('avg.contrib'), and concordance of node contribution ('cor.contrib') are all calculated from the data used to infer the network (provided in the 'data' argument). They are calculated from the module's summary profile. This is the eigenvector of the 1st principal component across all observations for every node composing the module. For gene coexpression modules this can be interpreted as a "summary expression profile". It is typically referred to as the "module eigengene" in the weighted gene coexpression network analysis literature (4).

The module coherence ('coherence') quantifies the proportion of module variance explained by the module's "summary profile". The higher this value, the more "coherent" the data is, i.e. the more similar the observations are nodes for each sample. With the default alternate hypothesis, a small permutation P-value indicates that the module is more coherent than expected by chance.

The average node contribution ('avg.contrib') and concordance of node contribution ('cor.contrib') are calculated from the node contribution, which quantifies how similar each node is to the modules's summary profile. It is calculated as the Pearson correlation coefficient between each node and the module summary profile. In the weighted gene coexpression network literature it is typically called the "module membership" (2).

The average node contribution ('avg.contrib') quantifies how similar nodes are to the module summary profile in the test dataset. Nodes detract from this score where the sign of their node contribution flips between the discovery and test datasets, e.g. in the case of differential gene expression across conditions. A high average node contribution with a small permutation P-value indicates that the module remains coherent in the test dataset, and that the nodes are acting together in a similar way.

The concordance of node contribution ('cor.contrib') measures whether the relative rank of nodes (in terms of their node contribution) is preserved across datasets. If a module is coherent enough that all nodes contribute strongly, then this statistic will not be meaningful as its value will be heavily influenced by tiny variations in node rank. This can be assessed through visualisation of the module topology (see plotContribution.) Similarly, a strong 'cor.contrib' is unlikely to be meaningful if the 'avg.contrib' is not significant.

The concordance of correlation strucutre ('cor.cor') and density of correlation structure ('avg.cor') are calculated from the user-provided correlation structure between nodes (provided in the 'correlation' argument). This is referred to as "coexpression" when calculated on gene expression data.

The 'avg.cor' measures how strongly nodes within a module are correlation on average in the test dataset. This average depends on the correlation coefficients in the discovery dataset: the score is penalised where correlation coefficients change in sign between datasets. A high 'avg.cor' with a small permutation P-value indicates that the module is (a) more strongly correlated than expected by chance for a module of the same size, and (b) more consistently correlated with respect to the discovery dataset than expected by chance.

The 'cor.cor' measures how similar the correlation coefficients are across the two datasets. A high 'cor.cor' with a small permutation P-value indicates that the correlation structure within a module is more similar across datasets than expected by chance. If all nodes within a module are very similarly correlated then this statistic will not be meaningful, as its value will be heavily influenced by tiny, non-meaningful, variations in correlation strength. This can be assessed through visualisation of the module topology (see plotCorrelation.) Similarly, a strong 'cor.cor' is unlikely to be meaningful if the 'avg.cor' is not significant.

The average edge weight ('avg.weight') and concordance of weighted degree ('cor.degree') are both calculated from the interaction network (provided as adjacency matrices to the 'network' argument).

The 'avg.weight' measures the average connection strength between nodes in the test dataset. In the weighted gene coexpression network literature this is typically called the "module density" (2). A high 'avg.weight' with a small permutation P-value indicates that the module is more strongly connected in the test dataset than expected by chance.

The 'cor.degree' calculates whether the relative rank of each node's weighted degree is similar across datasets. The weighted degree is calculated as the sum of a node's edge weights to all other nodes in the module. In the weighted gene coexpression network literature this is typically called the "intramodular connectivity" (2). This statistic will not be meaningful where all nodes are connected to each other with similar strength, as its value will be heavily influenced by tiny, non-meaningful, variations in weighted degree. This can be assessed through visualisation of the module topology (see plotDegree.)

Both the 'avg.weight' and 'cor.degree' assume edges are weighted, and that the network is densely connected. Note that for sparse networks, edges with zero weight are included when calculating both statistics. Only the magnitude of the weights, not their sign, contribute to the score. If the network is unweighted, i.e. edges indicate presence or absence of a relationship, then the 'avg.weight' will be the proportion of the number of edges to the total number of possible edges while the weighted degree simply becomes the degree. A high 'avg.weight' in this case measures how interconnected a module is in the test dataset. A high degree indicates that a node is connected to many other nodes. The interpretation of the 'cor.degree' remains unchanged between weighted and unweighted networks. If the network is directed the interpretation of the 'avg.weight' remains unchanged, while the cor.degree will measure the concordance of the node in-degree in the test network. To measure the out-degree instead, the adjacency matrices provided to the 'network' argument should be transposed.

Sparse data:

Caution should be used when running NetRep on sparse data (i.e. where there are many zero values in the data used to infer the network). For this data, the average node contribution ('avg.contrib'), concordance of node contribution ('cor.contrib'), and module coherence ('coherence') will all be systematically underestimated due to their reliance on the Pearson correlation coefficient to calculate the node contribution.

Care should also be taken to use appropriate methods for inferring the correlation structure when the data is sparse for the same reason.

Proportional data:

Caution should be used when running NetRep on proportional data ( i.e. where observations across samples all sum to the same value, e.g. 1). For this data, the average node contribution ('avg.contrib'), concordance of node contribution ('cor.contrib'), and module coherence ('coherence') will all be systematically overestimated due to their reliance on the Pearson correlation coefficient to calculate the node contribution.

Care should also be taken to use appropriate methods for inferring the correlation structure from proportional data for the same reason.

Hypothesis testing:

Three alternative hypotheses are available. "greater", the default, tests whether each module preservation statistic is larger than expected by chance. "lesser" tests whether each module preservation statistic is smaller than expected by chance, which may be useful for identifying modules that are extremely different in the test dataset. "two.sided" can be used to test both alternate hypotheses.

To determine whether a module preservation statistic deviates from chance, a permutation procedure is employed. Each statistic is calculated between the module in the discovery dataset and nPerm random subsets of the same size in the test dataset in order to assess the distribution of each statistic under the null hypothesis.

Two models for the null hypothesis are available: "overlap", the default, only nodes that are present in both the discovery and test networks are used when generating null distributions. This is appropriate under an assumption that nodes that are present in the test dataset, but not present in the discovery dataset, are unobserved: that is, they may fall in the module(s) of interest in the discovery dataset if they were to be measured there. Alternatively, "all" will use all nodes in the test network when generating the null distributions.

The number of permutations required for any given significance threshold is approximately 1 / the desired significance for one sided tests, and double that for two-sided tests. This can be calculated with requiredPerms. When nPerm is not specified, the number of permutations is automatically calculated as the number required for a Bonferroni corrected significance threshold adjusting for the total number of tests for each statistic, i.e. the total number of modules to be analysed multiplied by the number of test datasets each module is tested in. Although assessing the replication of a small numberof modules calls for very few permutations, we recommend using no fewer than 1,000 as fewer permutations are unlikely to generate representative null distributions. Note: the assumption used by requiredPerms to determine the correct number of permtutations breaks down when assessing the preservation of modules in a very small dataset (e.g. gene sets in a dataset with less than 100 genes total). However, the reported p-values will still be accurate (see permutationTest) (3).

Value

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 also a list, containing the following objects:

• observed: A matrix of the observed values for the module preservation statistics. Rows correspond to modules, and columns to the module preservation statistics.

• nulls: A three dimensional array containing the values of the module preservation statistics evaluated on random permutation of module assignment in the test network. Rows correspond to modules, columns to the module preservation statistics, and the third dimension to the permutations.

• p.values: A matrix of p-values for the observed module preservation statistics as evaluated through a permutation test using the corresponding values in nulls.

• nVarsPresent: A vector containing the number of variables that are present in the test dataset for each module.

• propVarsPresent: A vector containing the proportion of variables present in the test dataset for each module. Modules where this is less than 1 should be investigated further before making judgements about preservation to ensure that the missing variables are not the most connected ones.

• contingency: If moduleAssignments are present for both the discovery and test datasets, then a contingency table showing the overlap between modules across datasets is returned. Rows correspond to modules in the discovery dataset, columns to modules in the test dataset.

When simplify = TRUE then the simplest possible structure will be returned. E.g. if module preservation is tested in only one dataset, then the returned list will have only the above elements.

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, e.g. results[["Dataset1"]][["Dataset2"]] indicates an analysis where modules discovered in "Dataset1" are assessed for preservation in "Dataset2". Dataset comparisons which have not been assessed will contain NULL.

References

1. Ritchie, S.C., et al., A scalable permutation approach reveals replication and preservation patterns of network modules in large datasets. Cell Systems. 3, 71-82 (2016).

2. Langfelder, P., Luo, R., Oldham, M. C. & Horvath, S. Is my network module preserved and reproducible? PLoS Comput. Biol. 7, e1001057 (2011).

3. Phipson, B. & Smyth, G. K. Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. 9, Article39 (2010).

4. Langfelder, P. & Horvath, S. WGCNA: an R package for weighted correlation network analysis. BMC Bioinformatics 9, 559 (2008).

See Also

Functions for: visualising network modules, calculating module topology, calculating permutation test P-values, and splitting computation over multiple machines.

Examples

# 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)

# Assess module preservation: you should run at least 10,000 permutations
preservation <- modulePreservation(
 network=network_list, data=data_list, correlation=correlation_list, 
 moduleAssignments=labels_list, nPerm=1000, discovery="discovery", 
 test="test", nThreads=2
)

Calculate the topological properties for a network module

Description

Calculates the network properties used to assess module preservation for one or more modules in a user specified dataset.

Usage

networkProperties(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  simplify = TRUE,
  verbose = TRUE
)

Arguments

Details

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 networkProperties 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.

Value

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. Each of these is a list containing the following objects:

• 'degree': The weighted within-module degree: the sum of edge weights for each node in the module.

• 'avgWeight': The average edge weight within the module.

If the 'data' used to infer the 'test' network is provided then the following are also returned:

• 'summary': A vector summarising the module across each sample. This is calculated as the first eigenvector of the module from a principal component analysis.

• 'contribution': The node contribution: the similarity between each node and the module summary profile ('summary').

• 'coherence': The proportion of module variance explained by the 'summary' vector.

When simplify = TRUE then the simplest possible structure will be returned. E.g. if the network properties are requested for only one module in only one dataset, then the returned list will have only the above elements.

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 properties of "module1" as calculated in "Dataset2", where "module1" was indentified in "Dataset1". Modules and datasets for which calculation of the network properties have not been requested will contain NULL.

See Also

Getting nodes ordered by degree., and Ordering samples by module summary

Examples

# 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)

# Calculate the topological properties of all network modules in the discovery dataset
props <- networkProperties(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list
)
  
# Calculate the topological properties in the test dataset for the same modules
test_props <- networkProperties(
  network=network_list, data=data_list, correlation=correlation_list,
  moduleAssignments=labels_list, discovery="discovery", test="test"
)

Order nodes in descending order of weighted degree and order modules by the similarity of their summary vectors.

Description

Order nodes in descending order of weighted degree and order modules by the similarity of their summary vectors.

Usage

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

Arguments

Details

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 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.

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.

Mean weighted degree:

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.

Value

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.

References

1. Langfelder, P., Mischel, P. S. & Horvath, S. When is hub gene selection better than standard meta-analysis? PLoS One 8, e61505 (2013).

See Also

networkProperties

Examples

# 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
)

Template parameters

Description

Template parameters to be imported into other function documentation. This is not intended to be a stand-alone help file.

Arguments

Permutation test P-values for module preservation statistics

Description

Evaluates the statistical significance of each module preservation test statistic for one or more modules.

Usage

permutationTest(
  nulls,
  observed,
  nVarsPresent,
  totalSize,
  alternative = "greater"
)

Arguments

Details

Calculates exact p-values for permutation tests when permutations are randomly drawn with replacement using the permp function in the statmod package.

This function may be useful for re-calculating permutation test P-values, for example when there are missing values due to sparse data. In this case the user may decide that these missing values should be assigned 0 so that P-values aren't signficant purely due to many incalculable statistics leading to low power.

References

1. Phipson, B. & Smyth, G. K. Permutation P-values should never be zero: calculating exact P-values when permutations are randomly drawn. Stat. Appl. Genet. Mol. Biol. 9, Article39 (2010).

Examples

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)

# Note that we recommend running at least 10,000 permutations to make sure 
# that the null distributions are representative.

preservation <- modulePreservation(
 network=network_list, data=data_list, correlation=correlation_list, 
 moduleAssignments=labels_list, nPerm=1000, discovery="discovery", 
 test="test"
)

# Re-calculate the permutation test P-values
p.values <- permutationTest(
  preservation$nulls, preservation$observed, preservation$nVarsPresent,
  preservation$totalSize, preservation$alternative
)

Plot the topology of a network module

Description

Plot the correlation structure, network edges, scaled weighted degree, node contribtuion, module data, and module summary vectors of one or more network modules.

Individual components of the module plot can be plotted using plotCorrelation, plotNetwork, plotDegree, plotContribution, plotData, and plotSummary.

Usage

plotModule(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderSamplesBy = NULL,
  orderNodesBy = NULL,
  orderModules = TRUE,
  plotNodeNames = TRUE,
  plotSampleNames = TRUE,
  plotModuleNames = NULL,
  main = "Module Topology",
  main.line = 1,
  drawBorders = FALSE,
  lwd = 1,
  naxt.line = -0.5,
  saxt.line = -0.5,
  maxt.line = NULL,
  xaxt.line = -0.5,
  xaxt.tck = -0.025,
  xlab.line = 2.5,
  yaxt.line = 0,
  yaxt.tck = -0.15,
  ylab.line = 2.5,
  laxt.line = 2.5,
  laxt.tck = 0.04,
  cex.axis = 0.8,
  legend.main.line = 1.5,
  cex.lab = 1.2,
  cex.main = 2,
  dataCols = NULL,
  dataRange = NULL,
  corCols = correlation.palette(),
  corRange = c(-1, 1),
  netCols = network.palette(),
  netRange = c(0, 1),
  degreeCol = "#feb24c",
  contribCols = c("#A50026", "#313695"),
  summaryCols = c("#1B7837", "#762A83"),
  naCol = "#bdbdbd",
  dryRun = FALSE
)

Arguments

Details

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 node and sample ordering is being calculated within the same dataset being visualised, 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.

Node, sample, and module ordering:

By default, nodes are ordered in decreasing order of weighted degree in the discovery dataset (see nodeOrder). Missing nodes are colored in grey. This facilitates the visual comparison of modules across datasets, as the node ordering will be preserved.

Alternatively, a vector containing the names or indices of one or more datasets can be provided to the orderNodesBy argument.

If a single dataset is provided, then nodes will be ordered in decreasing order of weighted degree in that dataset. Only nodes that are present in this dataset will be drawn when ordering nodes by a dataset that is not the discovery dataset for the requested modules(s).

If multiple datasets are provided then the weighted degree will be averaged across these datasets (see nodeOrder for more details). This is useful for obtaining a robust ordering of nodes by relative importance, assuming the modules displayed are preserved in those datasets.

Ordering of nodes by weighted degree can be suppressed by setting orderNodesBy to NA, in which case nodes will be ordered as in the matrices provided in the network, data, and correlation arguments.

When multiple modules are drawn, modules are ordered by the similarity of their summary vectors in the dataset(s) specified in orderNodesBy argument. If multiple datasets are provided to the orderNodesBy argument then the module summary vectors are concatenated across datasets.

By default, samples in the data heatmap and accompanying module summary bar plot are ordered in descending order of module summary in the drawn dataset (specified by the test argument). If multiple modules are drawn, samples are ordered as per the left-most module on the plot.

Alternatively, a vector containing the name or index of another dataset may be provided to the orderSamplesBy argument. In this case, samples will be ordered in descending order of module summary in the specified dataset. This is useful when comparing different measurements across samples, for example, gene expression data obtained from multiple tissues samples across the same individuals. If the dataset specified is the discovery dataset, then missing samples will be displayed as horizontal grey bars. If the dataset specified is one of the other datasets, samples present in both the specified dataset and the test dataset will be displayed first in order of the specified dataset, then samples present in only the test dataset will be displayed underneath a horizontal black line ordered by their module summary vector in the test dataset.

Order of samples by module summary can be suppressed by setting orderSamplesBy to NA, in which case samples will be order as in the matrix provided to the data argument for the drawn dataset.

Weighted degree scaling:

When drawn on a plot, the weighted degree of each node is scaled to the maximum weighted degree within its module. The scaled weighted degree is measure of relative importance for each node to its module. This makes visualisation of multiple modules with different sizes and densities possible. However, the scaled weighted degree should only be interpreted for groups of nodes that have an apparent module structure.

Plot layout and device size

For optimal results we recommend viewing single modules on a PNG device with a width of 1500, a height of 2700 and a nominal resolution of 300 (png(filename, width=5*300, height=9*300, res=300))).

Warning: PDF and other vectorized devices should not be used when plotting more than a hundred nodes. Large files will be generated which may cause image editing programs such as Inkscape or Illustrator to crash when polishing figures for publication.

When dryRun is TRUE only the axes, legends, labels, and title will be drawn, allowing for quick iteration of customisable parameters to get the plot layout correct.

If axis labels or legends are drawn off screen then the margins of the plot should be adjusted prior to plotting using the par command to increase the margin size (see the "mar" option in the par help page).

The size of text labels can be modified by increasing or decreasing the cex.main, cex.lab, and cex.axis arguments:

• cex.main: controls the size of the plot title (specified in the main argument).

• cex.lab: controls the size of the axis labels on the weighted degree, node contribution, and module summary bar plots as well as the size of the module labels and the heatmap legend titles.

• cex.axis: contols the size of the axis tick labels, including the node and sample labels.

The position of these labels can be changed through the following arguments:

• xaxt.line: controls the distance from the plot the x-axis tick labels are drawn on the module summary bar plot.

• xlab.line: controls the distance from the plot the x-axis label is drawn on the module summary bar plot.

• yaxt.line: controls the distance from the plot the y-axis tick labels are drawn on the weighted degree and node contribution bar plots.

• ylab.line: controls the distance from the plot the y-axis label is drawn on the weighted degree and node contribution bar plots.

• main.line: controls the distance from the plot the title is drawn.

• naxt.line: controls the distance from the plot the node labels are drawn.

• saxt.line: controls the distance from the plot the sample labels are drawn.

• maxt.line: controls the distance from the plot the module labels are drawn.

• laxt.line: controls the distance from the heatmap legends that the gradient legend labels are drawn.

• legend.main.line: controls the distance from the heatmap legends that the legend title is drawn.

The rendering of node, sample, and module names can be disabled by setting plotNodeNames, plotSampleNames, and plotModuleNames to FALSE.

The size of the axis ticks can be changed by increasing or decreasing the following arguments:

• xaxt.tck: size of the x-axis tick labels as a multiple of the height of the module summary bar plot

• yaxt.tck: size of the y-axis tick labels as a multiple of the width of the weighted degree or node contribution bar plots.

• laxt.tck: size of the heatmap legend axis ticks as a multiple of the width of the data, correlation structure, or network edge weight heatmaps.

The drawBorders argument controls whether borders are drawn around the weighted degree, node contribution, or module summary bar plots. The lwd argument controls the thickness of these borders, as well as the thickness of axes and axis ticks.

Modifying the color palettes:

The dataCols and dataRange arguments control the appearance of the data heatmap (see plotData). The gradient of colors used on the heatmap can be changed by specifying a vector of colors to interpolate between in dataCols and dataRange specifies the range of values that maps to this gradient. Values outside of the specified dataRange will be rendered with the colors used at either extreme of the gradient. The default gradient is determined based on the data shown on the plot. If all values in the data matrix are positive, then the gradient is interpolated between white and green, where white is used for the smallest value and green for the largest. If all values are negative, then the gradient is interpolated between purple and white, where purple is used for the smallest value and white for the value closest to zero. If the data contains both positive and negative values, then the gradient is interpolated between purple, white, and green, where white is used for values of zero. In this case the range shown is always centered at zero, with the values at either extreme determined by the value in the rendered data with the strongest magnitude (the maximum of the absolute value).

The corCols and corRange arguments control the appearance of the correlation structure heatmap (see plotCorrelation). The gradient of colors used on the heatmap can be changed by specifying a vector of colors to interpolate between in corCols. By default, strong negative correlations are shown in blue, and strong positive correlations in red, and weak correlations as white. corRange controls the range of values that this gradient maps to, by default, -1 to 1. Changing this may be useful for showing differences where range of correlation coefficients is small.

The netCols and netRange arguments control the appearance of the network edge weight heatmap (see plotNetwork). The gradient of colors used on the heatmap can be changed by specifying a vector of colors to interpolate between in netCols. By default, weak or non-edges are shown in white, while strong edges are shown in red. The netRange controls the range of values this gradient maps to, by default, 0 to 1. If netRange is set to NA, then the gradient will be mapped to values between 0 and the maximum edge weight of the shown network.

The degreeCol argument controls the color of the weighted degree bar plot (see plotDegree).

The contribCols argument controls the color of the node contribution bar plot (see plotContribution. This can be specified as single value to be used for all nodes, or as two colors: one to use for nodes with positive contributions and one to use for nodes with negative contributions.

The summaryCols argument controls the color of the module summary bar plot (see plotSummary. This can be specified as single value to be used for all samples, or as two colors: one to use for samples with a positive module summary value and one fpr samples with a negative module summary value.

The naCol argument controls the color of missing nodes and samples on the data, correlaton structure, and network edge weight heatmaps.

Embedding in Rmarkdown documents

The chunk option fig.keep="last" should be set to avoid an empty plot being embedded above the plot generated by plotModule. This empty plot is generated so that an error will be thrown as early as possible if the margins are too small to be displayed. Normally, these are drawn over with the actual plot components when drawing the plot on other graphical devices.

See Also

plotCorrelation, plotNetwork, plotDegree, plotContribution, plotData, and plotSummary.

Examples

# 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)

# Plot module 1, 2 and 4 in the discovery dataset
plotModule(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 2, 4)
)

# Now plot them in the test dataset (module 2 does not replicate)
plotModule(
  network=network_list,data=data_list, correlation=correlation_list,
  moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
  test="test"
)

# Plot modules 1 and 4, which replicate, in the test datset ordering nodes
# by weighted degree averaged across the two datasets
plotModule(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 4), discovery="discovery",
  test="test", orderNodesBy=c("discovery", "test")
)

Plot a topological feature of network module

Description

Functions for plotting the topology of a network module.

Usage

plotData(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderSamplesBy = NULL,
  orderNodesBy = NULL,
  orderModules = TRUE,
  plotNodeNames = TRUE,
  plotSampleNames = TRUE,
  plotModuleNames = NULL,
  main = "",
  main.line = 1,
  lwd = 1,
  plotLegend = TRUE,
  legend.main = "Data",
  legend.main.line = 1,
  naxt.line = -0.5,
  saxt.line = -0.5,
  maxt.line = 3,
  legend.position = 0.15,
  laxt.tck = 0.03,
  laxt.line = 3,
  cex.axis = 0.8,
  cex.lab = 1.2,
  cex.main = 2,
  dataCols = NULL,
  dataRange = NULL,
  naCol = "#bdbdbd",
  dryRun = FALSE
)

plotCorrelation(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderNodesBy = NULL,
  symmetric = FALSE,
  orderModules = TRUE,
  plotNodeNames = TRUE,
  plotModuleNames = NULL,
  main = "",
  main.line = 1,
  lwd = 1,
  plotLegend = TRUE,
  legend.main = "Correlation",
  legend.main.line = 1,
  naxt.line = -0.5,
  maxt.line = 3,
  legend.position = NULL,
  laxt.tck = NULL,
  laxt.line = NULL,
  cex.axis = 0.8,
  cex.lab = 1.2,
  cex.main = 2,
  corCols = correlation.palette(),
  corRange = c(-1, 1),
  naCol = "#bdbdbd",
  dryRun = FALSE
)

plotNetwork(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderNodesBy = NULL,
  symmetric = FALSE,
  orderModules = TRUE,
  plotNodeNames = TRUE,
  plotModuleNames = NULL,
  main = "",
  main.line = 1,
  lwd = 1,
  plotLegend = TRUE,
  legend.main = "Edge weight",
  legend.main.line = 1,
  naxt.line = -0.5,
  maxt.line = 3,
  legend.position = NULL,
  laxt.tck = NULL,
  laxt.line = NULL,
  cex.axis = 0.8,
  cex.lab = 1.2,
  cex.main = 2,
  netCols = network.palette(),
  netRange = c(0, 1),
  naCol = "#bdbdbd",
  dryRun = FALSE
)

plotContribution(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderNodesBy = NULL,
  orderModules = TRUE,
  plotNodeNames = TRUE,
  plotModuleNames = NULL,
  main = "",
  main.line = 1,
  ylab.line = 2.5,
  lwd = 1,
  drawBorders = FALSE,
  naxt.line = -0.5,
  maxt.line = 3,
  yaxt.line = 0,
  yaxt.tck = -0.035,
  cex.axis = 0.8,
  cex.lab = 1.2,
  cex.main = 2,
  contribCols = c("#A50026", "#313695"),
  naCol = "#bdbdbd",
  dryRun = FALSE
)

plotDegree(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderNodesBy = NULL,
  orderModules = TRUE,
  plotNodeNames = TRUE,
  plotModuleNames = NULL,
  main = "",
  main.line = 1,
  lwd = 1,
  drawBorders = FALSE,
  naxt.line = -0.5,
  maxt.line = 3,
  yaxt.line = 0,
  yaxt.tck = -0.035,
  ylab.line = 2.5,
  cex.axis = 0.8,
  cex.lab = 1.2,
  cex.main = 2,
  degreeCol = "#feb24c",
  naCol = "#bdbdbd",
  dryRun = FALSE
)

plotSummary(
  network,
  data,
  correlation,
  moduleAssignments = NULL,
  modules = NULL,
  backgroundLabel = "0",
  discovery = NULL,
  test = NULL,
  verbose = TRUE,
  orderSamplesBy = NULL,
  orderNodesBy = NULL,
  orderModules = TRUE,
  plotSampleNames = TRUE,
  plotModuleNames = NULL,
  main = "",
  main.line = 1,
  xlab.line = 2.5,
  lwd = 1,
  drawBorders = FALSE,
  saxt.line = -0.5,
  maxt.line = 0,
  xaxt.line = 0,
  xaxt.tck = -0.025,
  cex.axis = 0.8,
  cex.lab = 1.2,
  cex.main = 2,
  summaryCols = c("#1B7837", "#762A83"),
  naCol = "#bdbdbd",
  dryRun = FALSE
)

Arguments

Details

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 node and sample ordering is being calculated within the same dataset being visualised, 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.

Node, sample, and module ordering:

By default, nodes are ordered in decreasing order of weighted degree in the discovery dataset (see nodeOrder). Missing nodes are colored in grey. This facilitates the visual comparison of modules across datasets, as the node ordering will be preserved.

Alternatively, a vector containing the names or indices of one or more datasets can be provided to the orderNodesBy argument.

If a single dataset is provided, then nodes will be ordered in decreasing order of weighted degree in that dataset. Only nodes that are present in this dataset will be drawn when ordering nodes by a dataset that is not the discovery dataset for the requested modules(s).

If multiple datasets are provided then the weighted degree will be averaged across these datasets (see nodeOrder for more details). This is useful for obtaining a robust ordering of nodes by relative importance, assuming the modules displayed are preserved in those datasets.

Ordering of nodes by weighted degree can be suppressed by setting orderNodesBy to NA, in which case nodes will be ordered as in the matrices provided in the network, data, and correlation arguments.

When multiple modules are drawn, modules are ordered by the similarity of their summary vectors in the dataset(s) specified in orderNodesBy argument. If multiple datasets are provided to the orderNodesBy argument then the module summary vectors are concatenated across datasets.

By default, samples in the data heatmap and accompanying module summary bar plot are ordered in descending order of module summary in the drawn dataset (specified by the test argument). If multiple modules are drawn, samples are ordered as per the left-most module on the plot.

Alternatively, a vector containing the name or index of another dataset may be provided to the orderSamplesBy argument. In this case, samples will be ordered in descending order of module summary in the specified dataset. This is useful when comparing different measurements across samples, for example, gene expression data obtained from multiple tissues samples across the same individuals. If the dataset specified is the discovery dataset, then missing samples will be displayed as horizontal grey bars. If the dataset specified is one of the other datasets, samples present in both the specified dataset and the test dataset will be displayed first in order of the specified dataset, then samples present in only the test dataset will be displayed underneath a horizontal black line ordered by their module summary vector in the test dataset.

Order of samples by module summary can be suppressed by setting orderSamplesBy to NA, in which case samples will be order as in the matrix provided to the data argument for the drawn dataset.

Weighted degree scaling:

When drawn on a plot, the weighted degree of each node is scaled to the maximum weighted degree within its module. The scaled weighted degree is measure of relative importance for each node to its module. This makes visualisation of multiple modules with different sizes and densities possible. However, the scaled weighted degree should only be interpreted for groups of nodes that have an apparent module structure.

Plot layout and device size

Although reasonable default values for most parameters have been provided, the rendering of axes and titles may need adjusting depending on the size of the plot window. The dryRun argument is useful for quickly determining whether the plot will render correctly. When dryRun is TRUE only the axes, legends, labels, and title will be drawn, allowing for quick iteration of customisable parameters to get the plot layout correct.

Warning: PDF and other vectorized devices should not be used when plotting the heatmaps with more than a hundred nodes. Large files will be generated which may cause image editing programs such as Inkscape or Illustrator to crash when polishing figures for publication.

If axis labels or legends are drawn off screen then the margins of the plot should be adjusted prior to plotting using the par command to increase the margin size (see the "mar" option in the par help page).

The size of text labels can be modified by increasing or decreasing the cex.main, cex.lab, and cex.axis arguments:

• cex.main: controls the size of the plot title (specified in the main argument).

• cex.lab: controls the size of the axis labels on the weighted degree, node contribution, and module summary bar plots as well as the size of the module labels and the heatmap legend titles.

• cex.axis: contols the size of the axis tick labels, including the node and sample labels.

The position of these labels can be changed through the following arguments:

• xaxt.line: controls the distance from the plot the x-axis tick labels are drawn on the module summary bar plot.

• xlab.line: controls the distance from the plot the x-axis label is drawn on the module summary bar plot.

• yaxt.line: controls the distance from the plot the y-axis tick labels are drawn on the weighted degree and node contribution bar plots.

• ylab.line: controls the distance from the plot the y-axis label is drawn on the weighted degree and node contribution bar plots.

• main.line: controls the distance from the plot the title is drawn.

• naxt.line: controls the distance from the plot the node labels are drawn.

• saxt.line: controls the distance from the plot the sample labels are drawn.

• maxt.line: controls the distance from the plot the module labels are drawn.

• laxt.line: controls the distance from the heatmap legends that the gradient legend labels are drawn.

• legend.main.line: controls the distance from the heatmap legends that the legend title is drawn.

The rendering of node, sample, and module names can be disabled by setting plotNodeNames, plotSampleNames, and plotModuleNames to FALSE.

The size of the axis ticks can be changed by increasing or decreasing the following arguments:

• xaxt.tck: size of the x-axis tick labels as a multiple of the height of the module summary bar plot

• yaxt.tck: size of the y-axis tick labels as a multiple of the width of the weighted degree or node contribution bar plots.

• laxt.tck: size of the heatmap legend axis ticks as a multiple of the width of the data, correlation structure, or network edge weight heatmaps.

The placement of heatmap legends is controlled by the following arguments:

• plotLegend: if FALSE legend will not be drawn.

• legend.position: a multiple of the plot width, controls the horizontal distance from the plot the legend is drawn.

The drawBorders argument controls whether borders are drawn around the weighted degree, node contribution, or module summary bar plots. The lwd argument controls the thickness of these borders, as well as the thickness of axes and axis ticks.

Modifying the color palettes:

The dataCols and dataRange arguments control the appearance of the data heatmap (see plotData). The gradient of colors used on the heatmap can be changed by specifying a vector of colors to interpolate between in dataCols and dataRange specifies the range of values that maps to this gradient. Values outside of the specified dataRange will be rendered with the colors used at either extreme of the gradient. The default gradient is determined based on the data shown on the plot. If all values in the data matrix are positive, then the gradient is interpolated between white and green, where white is used for the smallest value and green for the largest. If all values are negative, then the gradient is interpolated between purple and white, where purple is used for the smallest value and white for the value closest to zero. If the data contains both positive and negative values, then the gradient is interpolated between purple, white, and green, where white is used for values of zero. In this case the range shown is always centered at zero, with the values at either extreme determined by the value in the rendered data with the strongest magnitude (the maximum of the absolute value).

The corCols and corRange arguments control the appearance of the correlation structure heatmap (see plotCorrelation). The gradient of colors used on the heatmap can be changed by specifying a vector of colors to interpolate between in corCols. By default, strong negative correlations are shown in blue, and strong positive correlations in red, and weak correlations as white. corRange controls the range of values that this gradient maps to, by default, -1 to 1. Changing this may be useful for showing differences where range of correlation coefficients is small.

The netCols and netRange arguments control the appearance of the network edge weight heatmap (see plotNetwork). The gradient of colors used on the heatmap can be changed by specifying a vector of colors to interpolate between in netCols. By default, weak or non-edges are shown in white, while strong edges are shown in red. The netRange controls the range of values this gradient maps to, by default, 0 to 1. If netRange is set to NA, then the gradient will be mapped to values between 0 and the maximum edge weight of the shown network.

The degreeCol argument controls the color of the weighted degree bar plot (see plotDegree).

The contribCols argument controls the color of the node contribution bar plot (see plotContribution. This can be specified as single value to be used for all nodes, or as two colors: one to use for nodes with positive contributions and one to use for nodes with negative contributions.

The summaryCols argument controls the color of the module summary bar plot (see plotSummary. This can be specified as single value to be used for all samples, or as two colors: one to use for samples with a positive module summary value and one fpr samples with a negative module summary value.

The naCol argument controls the color of missing nodes and samples on the data, correlaton structure, and network edge weight heatmaps.

See Also

plotModule for a combined plot showing all topological properties for a network module.

Examples

# 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)

# Plot the data for module 1, 2 and 4 in the discovery dataset
plotData(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 2, 4)
)

# Symmetric = TRUE gives a traditional heatmap for the correlation structure
# and weighted network
plotCorrelation(
  network=network_list, data=data_list, correlation=correlation_list,
  moduleAssignments=labels_list, modules=c(1, 2, 4), symmetric=TRUE
)

# While the default is to render only one half of the (symmetric) matrix
plotNetwork(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 2, 4)
)

# Plot the degree of nodes in each module in the test dataset, but show them
# in the same order as the discovery dataset to compare how node degree 
# changes
plotDegree(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
  test="test"
)

# Alternatively nodes can be ordered on the plot by degree in the test dataset
plotDegree(
  network=network_list, data=data_list, correlation=correlation_list,
  moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
  test="test", orderNodesBy="test"
)

# Or by averaging the degree across datasets for a more robust ordering  
plotDegree(
 network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
  test="test", orderNodesBy=c("discovery", "test")
)

# Arbitrary subsets can be plotted:
plotContribution(
  network=network_list[[1]][1:10, 1:10], data=data_list[[1]][, 1:10], 
  correlation=correlation_list[[1]][1:10, 1:10], orderNodesBy=NA
)

# Plot the module summary vectors for multiple modules:
plotSummary(
  network=network_list, data=data_list, correlation=correlation_list, 
  moduleAssignments=labels_list, modules=c(1, 2, 4), discovery="discovery",
  test="test", orderSamplesBy="test"
)

Template parameters

Description

Template parameters to be imported into other function documentation. This is not intended to be a stand-alone help file.

Arguments

How many permutations do I need to test at my desired significance level?

Description

How many permutations do I need to test at my desired significance level?

Usage

requiredPerms(alpha, alternative = "greater")

Arguments

Value

The minimum number of permutations required to detect any significant associations at the provided alpha. The minimum p-value will always be smaller than alpha.

Examples

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)

# How many permutations are required to Bonferroni adjust for the 4 modules 
# in the example data? 
nPerm <- requiredPerms(0.05/4) 

# Note that we recommend running at least 10,000 permutations to make sure 
# that the null distributions are representative.

preservation <- modulePreservation(
 network=network_list, data=data_list, correlation=correlation_list, 
 moduleAssignments=labels_list, nPerm=nPerm, discovery="discovery", 
 test="test"
)

Order samples within a network.

Description

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

Usage

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

Arguments

Details

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.

Value

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.

See Also

networkProperties

Examples

# 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" 
)

Template parameters

Description

Template parameters to be imported into other function documentation. This is not intended to be a stand-alone help file.

Arguments