# Differential Network Enrichment Analysis for Biological Data

## Documentation for package ‘DNEA’ version 1.1.0

- DESCRIPTION file
- User guides, package vignettes and other documentation
- Package NEWS
- Package README

## Help Pages

DNEA-package | DNEA: Differential Network Enrichment Analysis for Biological Data
addExpressionData | Include custom normalized data in the DNEA object
adjacencyGraph | Retrieve the adjacency graph for the case, control, or joint network
adjacencyGraph-method | Retrieve the adjacency graph for the case, control, or joint network
adjacencyMatrix | Retrieve the weighted or unweighted adjacency matrix
adjacencyMatrix-method | Retrieve the weighted or unweighted adjacency matrix
aggregateFeatures | Aggregate correlated features into a single feature class
BICscores | Access the BIC scores for each lambda value evaluated
BICscores-method | Access the BIC scores for each lambda value evaluated
BICscores<- | Access the BIC scores for each lambda value evaluated
BICscores<--method | Access the BIC scores for each lambda value evaluated
BICtune | Optimize the lambda regularization parameter for the glasso-based network models using Bayesian-information Criterion
BICtune-method | Optimize the lambda regularization parameter for the glasso-based network models using Bayesian-information Criterion
CCsummary | Retrieves the summary results of consensus clustering
CCsummary-method | Retrieves the summary results of consensus clustering
clusterNet | Identify metabolic modules within the biological networks using a consensus clustering approach
collapsed_DNEA | collapsed_DNEA
collapsed_DNEA-class | collapsed_DNEA
consensusClusteringResults | consensusClusteringResults
consensusClusteringResults-class | consensusClusteringResults
createDNEAobject | Initialize a DNEA object
datasetSummary | Access the dataset_summary slot of a DNEA object
datasetSummary-method | Access the dataset_summary slot of a DNEA object
diagnostics | Retrieve the diagnostic values for the input expression data
diagnostics-method | Retrieve the diagnostic values for the input expression data
DNEA | DNEA: Differential Network Enrichment Analysis for Biological Data
DNEA-class | DNEA object
DNEAinputSummary | DNEAinputSummary
DNEAinputSummary-class | DNEAinputSummary
dnw | Example results for DNEA
edgeList | Access the edge list
edgeList-method | Access the edge list
edgeList<- | Access the edge list
edgeList<--method | Access the edge list
expressionData | Access expression data within a DNEA object,
expressionData-method | Access expression data within a DNEA object,
featureNames | Retrieve the feature names from the metadata slot.
featureNames-method | Retrieve the feature names from the metadata slot.
filterNetworks | Filter the adjacency matrices to only the edges that meet the filter conditions
filterNetworks-method | Filter the adjacency matrices to only the edges that meet the filter conditions
getNetworkFiles | Save network information to .csv files
getNetworks | Construct the GLASSO-based biological Networks
includeMetadata | Add additional metadata to the DNEA object
lambdas2Test | Access the lambda values tested during hyper parameter optimization
lambdas2Test-method | Access the lambda values tested during hyper parameter optimization
massDataset2DNEA | Initialize a DNEA object from a mass_dataset object
metab_data | Feature meta data for the The Environmental Determinants of Diabetes in the Young (TEDDY) clinical trial
metaData | Retrieve metadata stored in a DNEA
metaData-method | Retrieve metadata stored in a DNEA
netGSAresults | Access the netGSA slot of a DNEA object
netGSAresults-method | Access the netGSA slot of a DNEA object
networkGroupIDs | Access and set the experimental group labels
networkGroupIDs-method | Access and set the experimental group labels
networkGroupIDs<- | Access and set the experimental group labels
networkGroups | Retrieve the unique group values of the experimental condition
networkGroups-method | Retrieve the unique group values of the experimental condition
nodeList | Access the node list
nodeList-method | Access the node list
nodeList<- | Access the node list
nodeList<--method | Access the node list
numFeatures | Retrieve the total number of features in the dataset
numFeatures-method | Retrieve the total number of features in the dataset
numSamples | Retrieves the total number of samples in the dataset
numSamples-method | Retrieves the total number of samples in the dataset
optimizedLambda | Access the lambda value used in analysis
optimizedLambda-method | Access the lambda value used in analysis
optimizedLambda<- | Access the lambda value used in analysis
optimizedLambda<--method | Access the lambda value used in analysis
plotNetworks | Visualize the biological networks
projectName | Return the name of the current experiment
projectName-method | Return the name of the current experiment
runNetGSA | Identify metabolic modules that are enriched across experimental conditions using NetGSA
sampleNames | Retrieve the sample names from the metadata slot.
sampleNames-method | Retrieve the sample names from the metadata slot.
selectionProbabilities | Access and set the edge selection probabilities from stabilitySelection()
selectionProbabilities-method | Access and set the edge selection probabilities from stabilitySelection()
selectionResults | Access and set the edge selection results from stabilitySelection()
selectionResults-method | Access and set the edge selection results from stabilitySelection()
show-method | DNEA object
show-method | DNEAinputSummary
stabilitySelection | Stability selection calculates selection probabilities for every possible feature-feature interaction within the input data
subnetworkMembership | Retrieve the subnetwork membership for each feature
subnetworkMembership-method | Retrieve the subnetwork membership for each feature
sumExp2DNEA | Initialize a DNEA from SummarizedExperiment
T1Dmeta | Sample meta data for the The Environmental Determinants of Diabetes in the Young (TEDDY) clinical trial
TEDDY | Example expresion data set from The Environmental Determinants of Diabetes in the Young (TEDDY) clinical trial