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  "Title": "Differential Network Enrichment Analysis for Biological Data",
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  "Description": "The DNEA R package is the latest implementation of the\nDifferential Network Enrichment Analysis algorithm and is the\nsuccessor to the Filigree Java-application described in Iyer et\nal. (2020). The package is designed to take as input an m x n\nexpression matrix for some -omics modality (ie. metabolomics,\nlipidomics, proteomics, etc.) and jointly estimate the\nbiological network associations of each condition using the\nDNEA algorithm described in Ma et al. (2019). This approach\nprovides a framework for data-driven enrichment analysis across\ntwo experimental conditions that utilizes the underlying\ncorrelation structure of the data to determine feature-feature\ninteractions.",
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  "Date/Publication": "2026-04-28 13:05:05 UTC",
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      "page": "DNEA-package",
      "title": "DNEA: Differential Network Enrichment Analysis for Biological Data",
      "topics": [
        "DNEA-package",
        "DNEA"
      ]
    },
    {
      "page": "addExpressionData",
      "title": "Include custom normalized data in the DNEA object",
      "topics": [
        "addExpressionData"
      ]
    },
    {
      "page": "adjacencyGraph-methods",
      "title": "Retrieve the adjacency graph for the case, control, or joint network",
      "topics": [
        "adjacencyGraph",
        "adjacencyGraph,consensusClusteringResults-method",
        "adjacencyGraph,DNEA-method"
      ]
    },
    {
      "page": "adjacencyMatrix-methods",
      "title": "Retrieve the weighted or unweighted adjacency matrix",
      "topics": [
        "adjacencyMatrix",
        "adjacencyMatrix,DNEA-method"
      ]
    },
    {
      "page": "aggregateFeatures",
      "title": "Aggregate correlated features into a single feature class",
      "topics": [
        "aggregateFeatures"
      ]
    },
    {
      "page": "BICscores-methods",
      "title": "Access the BIC scores for each lambda value evaluated",
      "topics": [
        "BICscores",
        "BICscores,DNEA-method",
        "BICscores<-",
        "BICscores<-,DNEA-method"
      ]
    },
    {
      "page": "BICtune-methods",
      "title": "Optimize the lambda regularization parameter for the glasso-based network models using Bayesian-information Criterion",
      "topics": [
        "BICtune",
        "BICtune,DNEA-method",
        "BICtune,matrix-method"
      ]
    },
    {
      "page": "CCsummary-methods",
      "title": "Retrieves the summary results of consensus clustering",
      "topics": [
        "CCsummary",
        "CCsummary,DNEA-method"
      ]
    },
    {
      "page": "clusterNet",
      "title": "Identify metabolic modules within the biological networks using a consensus clustering approach",
      "topics": [
        "clusterNet"
      ]
    },
    {
      "page": "collapsed_DNEA-class",
      "title": "collapsed_DNEA",
      "topics": [
        "collapsed_DNEA",
        "collapsed_DNEA-class"
      ]
    },
    {
      "page": "consensusClusteringResults-class",
      "title": "consensusClusteringResults",
      "topics": [
        "consensusClusteringResults",
        "consensusClusteringResults-class"
      ]
    },
    {
      "page": "createDNEAobject",
      "title": "Initialize a DNEA object",
      "topics": [
        "createDNEAobject"
      ]
    },
    {
      "page": "datasetSummary-methods",
      "title": "Access the dataset_summary slot of a DNEA object",
      "topics": [
        "datasetSummary",
        "datasetSummary,DNEA-method"
      ]
    },
    {
      "page": "diagnostics-methods",
      "title": "Retrieve the diagnostic values for the input expression data",
      "topics": [
        "diagnostics",
        "diagnostics,DNEA-method",
        "diagnostics,DNEAinputSummary-method"
      ]
    },
    {
      "page": "DNEA-class",
      "title": "DNEA object",
      "topics": [
        "DNEA-class",
        "show,DNEA-method"
      ]
    },
    {
      "page": "DNEAinputSummary-class",
      "title": "DNEAinputSummary",
      "topics": [
        "DNEAinputSummary",
        "DNEAinputSummary-class",
        "show,DNEAinputSummary-method"
      ]
    },
    {
      "page": "dnw",
      "title": "Example results for DNEA",
      "topics": [
        "dnw"
      ]
    },
    {
      "page": "edgeList-methods",
      "title": "Access the edge list",
      "topics": [
        "edgeList",
        "edgeList,DNEA-method",
        "edgeList<-",
        "edgeList<-,DNEA-method"
      ]
    },
    {
      "page": "expressionData-methods",
      "title": "Access expression data within a DNEA object,",
      "topics": [
        "expressionData",
        "expressionData,DNEA-method"
      ]
    },
    {
      "page": "featureNames-methods",
      "title": "Retrieve the feature names from the metadata slot.",
      "topics": [
        "featureNames",
        "featureNames,DNEA-method"
      ]
    },
    {
      "page": "filterNetworks-methods",
      "title": "Filter the adjacency matrices to only the edges that meet the filter conditions",
      "topics": [
        "filterNetworks",
        "filterNetworks,DNEA-method",
        "filterNetworks,list-method"
      ]
    },
    {
      "page": "getNetworkFiles",
      "title": "Save network information to .csv files",
      "topics": [
        "getNetworkFiles"
      ]
    },
    {
      "page": "getNetworks",
      "title": "Construct the GLASSO-based biological Networks",
      "topics": [
        "getNetworks"
      ]
    },
    {
      "page": "includeMetadata",
      "title": "Add additional metadata to the DNEA object",
      "topics": [
        "includeMetadata"
      ]
    },
    {
      "page": "lambdas2Test-methods",
      "title": "Access the lambda values tested during hyper parameter optimization",
      "topics": [
        "lambdas2Test",
        "lambdas2Test,DNEA-method"
      ]
    },
    {
      "page": "massDataset2DNEA",
      "title": "Initialize a DNEA object from a mass_dataset object",
      "topics": [
        "massDataset2DNEA"
      ]
    },
    {
      "page": "metab_data",
      "title": "Feature meta data for the The Environmental Determinants of Diabetes in the Young (TEDDY) clinical trial",
      "topics": [
        "metab_data"
      ]
    },
    {
      "page": "metaData-methods",
      "title": "Retrieve metadata stored in a DNEA",
      "topics": [
        "metaData",
        "metaData,DNEA-method"
      ]
    },
    {
      "page": "netGSAresults-methods",
      "title": "Access the netGSA slot of a DNEA object",
      "topics": [
        "netGSAresults",
        "netGSAresults,DNEA-method"
      ]
    },
    {
      "page": "networkGroupIDs-methods",
      "title": "Access and set the experimental group labels",
      "topics": [
        "networkGroupIDs",
        "networkGroupIDs,DNEA-method",
        "networkGroupIDs<-"
      ]
    },
    {
      "page": "networkGroups-methods",
      "title": "Retrieve the unique group values of the experimental condition",
      "topics": [
        "networkGroups",
        "networkGroups,DNEA-method"
      ]
    },
    {
      "page": "nodeList-methods",
      "title": "Access the node list",
      "topics": [
        "nodeList",
        "nodeList,DNEA-method",
        "nodeList<-",
        "nodeList<-,DNEA-method"
      ]
    },
    {
      "page": "numFeatures-methods",
      "title": "Retrieve the total number of features in the dataset",
      "topics": [
        "numFeatures",
        "numFeatures,DNEA-method",
        "numFeatures,DNEAinputSummary-method"
      ]
    },
    {
      "page": "numSamples-methods",
      "title": "Retrieves the total number of samples in the dataset",
      "topics": [
        "numSamples",
        "numSamples,DNEA-method",
        "numSamples,DNEAinputSummary-method"
      ]
    },
    {
      "page": "optimizedLambda-methods",
      "title": "Access the lambda value used in analysis",
      "topics": [
        "optimizedLambda",
        "optimizedLambda,DNEA-method",
        "optimizedLambda<-",
        "optimizedLambda<-,DNEA-method"
      ]
    },
    {
      "page": "plotNetworks",
      "title": "Visualize the biological networks",
      "topics": [
        "plotNetworks"
      ]
    },
    {
      "page": "projectName-methods",
      "title": "Return the name of the current experiment",
      "topics": [
        "projectName",
        "projectName,DNEA-method"
      ]
    },
    {
      "page": "runNetGSA",
      "title": "Identify metabolic modules that are enriched across experimental conditions using NetGSA",
      "topics": [
        "runNetGSA"
      ]
    },
    {
      "page": "sampleNames-methods",
      "title": "Retrieve the sample names from the metadata slot.",
      "topics": [
        "sampleNames",
        "sampleNames,DNEA-method"
      ]
    },
    {
      "page": "selectionProbabilities-methods",
      "title": "Access and set the edge selection probabilities from stabilitySelection()",
      "topics": [
        "selectionProbabilities",
        "selectionProbabilities,DNEA-method"
      ]
    },
    {
      "page": "selectionResults-methods",
      "title": "Access and set the edge selection results from stabilitySelection()",
      "topics": [
        "selectionResults",
        "selectionResults,DNEA-method"
      ]
    },
    {
      "page": "stabilitySelection",
      "title": "Stability selection calculates selection probabilities for every possible feature-feature interaction within the input data",
      "topics": [
        "stabilitySelection"
      ]
    },
    {
      "page": "subnetworkMembership-methods",
      "title": "Retrieve the subnetwork membership for each feature",
      "topics": [
        "subnetworkMembership",
        "subnetworkMembership,consensusClusteringResults-method",
        "subnetworkMembership,DNEA-method"
      ]
    },
    {
      "page": "sumExp2DNEA",
      "title": "Initialize a DNEA from SummarizedExperiment",
      "topics": [
        "sumExp2DNEA"
      ]
    },
    {
      "page": "T1Dmeta",
      "title": "Sample meta data for the The Environmental Determinants of Diabetes in the Young (TEDDY) clinical trial",
      "topics": [
        "T1Dmeta"
      ]
    },
    {
      "page": "TEDDY",
      "title": "Example expresion data set from The Environmental Determinants of Diabetes in the Young (TEDDY) clinical trial",
      "topics": [
        "TEDDY"
      ]
    }
  ],
  "_readme": "https://github.com/bioc/DNEA/raw/HEAD/README.md",
  "_rundeps": [
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    "Seqinfo",
    "shape",
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    "SparseArray",
    "stringi",
    "stringr",
    "SummarizedExperiment",
    "survival",
    "sys",
    "tibble",
    "tidyr",
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    "timechange",
    "utf8",
    "uuid",
    "vctrs",
    "withr",
    "XML",
    "xtable",
    "XVector"
  ],
  "_vignettes": [
    {
      "source": "DNEA.Rmd",
      "filename": "DNEA.html",
      "title": "Differential Network Expression Analysis for Metabolomics Data",
      "author": "Christopher P. Patsalis",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Downloading DNEA",
        "Basic DNEA workflow",
        "Example Data",
        "Quick Start",
        "Input data",
        "expression_data",
        "group_labels",
        "STEP 1: Data pre-processing and feature aggregation",
        "Data pre-processing",
        "Feature Aggregation",
        "[OPTIONAL] Custom-Normalized Data Input",
        "STEP 2: Model Tuning",
        "$\\lambda$ tuning via BIC",
        "Stability Selection",
        "Step 3: Constructing the Networks and Consensus Clustering",
        "Constructing the Networks",
        "Consensus Clustering",
        "Step 4: Pathway Enrichment via NetGSA and Network Visualization",
        "Pathway Enrichment via NetGSA",
        "Network Visualization",
        "Citation",
        "Session info",
        "References"
      ],
      "created": "2023-08-23 21:41:24",
      "modified": "2025-04-23 19:52:07",
      "commits": 29
    }
  ],
  "_score": 4.778151250383644,
  "_indexed": true,
  "_nocasepkg": "dnea",
  "_universes": [
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