CorNetto Workflow

Introduction

CorNetto builds knowledge-guided multi-omic correlation networks from normalized assay data stored in a MultiAssayExperiment. It is intended for workflows where transcriptomic, proteomic, metabolomic, or related assays are already normalized and the analysis question is whether groups differ in correlation structure, prior-supported connectivity, or node-level rewiring.

The package uses standard Bioconductor containers for assay and sample metadata, and returns standardized edge tables that can be combined, converted to igraph objects, or exported as Cytoscape-ready node and edge tables.

CorNetto is for downstream analysis after assay-level preprocessing. It uses Bioconductor containers, implements the Fisher z-difference test for differential correlation, and uses igraph for graph representation. The package adds the glue needed for prior-guided candidate-edge differential correlation, knowledge-network-aware integration, focused subnetworks, and rewiring summaries in a single reproducible workflow.

Relationship to existing Bioconductor software

The Bioconductor package dcanr provides several methods and an evaluation framework for differential co-expression or association network inference. CorNetto has a narrower role. It works directly with MultiAssayExperiment, tests prespecified within- or cross-assay edges from heterogeneous prior networks, and carries those results through network integration and node-level rewiring summaries. It is therefore complementary to dcanr rather than a replacement for its method-comparison and benchmarking framework.

Workflow overview

Green boxes are CorNetto functions, blue boxes are the objects they return, and orange boxes are inputs and the MultiAssayExperiment that carries results between steps.

The diagram outlines how the functions relate to one another. Inputs (yellow) are read either by readKnowledgeNetwork(), for prior-knowledge databases, or by createAnalysisData(), for multi-omic abundance data and sample metadata. Each returns a package-native object: a prior network, or a CorNetto MultiAssayExperiment. From there the package builds correlation networks, differential correlation networks, rewiring scores and permuted rewiring scores.

The order shown is illustrative, not prescriptive. You could equally run createDifferentialCorrelationNetwork(), narrow the result with filterNetworkByNodes(), then call createNetworkGraph() to obtain a focused differential-correlation graph.

The bottom-right panel shows the two ways results are handled: a function can return its result directly, or store it inside the CorNetto MultiAssayExperiment, from which getCorNettoResult() retrieves it by name. Both work; which you use is a matter of preference.

The CorNetto workflow, from input data and prior knowledge through to rewiring validation and export.

The CorNetto workflow, from input data and prior knowledge through to rewiring validation and export.

Version information

c(
    R = R.version.string,
    Bioconductor = as.character(BiocManager::version()),
    CorNetto = as.character(utils::packageVersion("CorNetto"))
)
#>                              R                   Bioconductor 
#> "R version 4.6.1 (2026-06-24)"                         "3.23" 
#>                       CorNetto 
#>                       "0.99.1"

Installation

After acceptance to Bioconductor, install CorNetto with:

if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
BiocManager::install("CorNetto")

Example data

analysisData <- exampleAnalysisData()
knowledgeNetwork <- exampleKnowledgeNetwork()
seedNodes <- exampleSeedNodes()

analysisData
#> A MultiAssayExperiment object of 3 listed
#>  experiments with user-defined names and respective classes.
#>  Containing an ExperimentList class object of length 3:
#>  [1] protein: SummarizedExperiment with 5 rows and 20 columns
#>  [2] transcript: SummarizedExperiment with 5 rows and 20 columns
#>  [3] metabolite: SummarizedExperiment with 4 rows and 20 columns
#> Functionality:
#>  experiments() - obtain the ExperimentList instance
#>  colData() - the primary/phenotype DataFrame
#>  sampleMap() - the sample coordination DataFrame
#>  `$`, `[`, `[[` - extract colData columns, subset, or experiment
#>  *Format() - convert into a long or wide DataFrame
#>  assays() - convert ExperimentList to a SimpleList of matrices
#>  exportClass() - save data to flat files
summarizeAnalysisData(analysisData, groupColumn = "clinicalGroup", quiet = TRUE)
#> $assays
#> DataFrame with 3 rows and 9 columns
#>     assayName featureCount sampleCount missingValueCount missingValueFraction
#>   <character>    <integer>   <integer>         <integer>            <numeric>
#> 1     protein            5          20                 0                    0
#> 2  transcript            5          20                 0                    0
#> 3  metabolite            4          20                 0                    0
#>   zeroVarianceFeatureCount allMissingFeatureCount highMissingFeatureCount
#>                  <integer>              <integer>               <integer>
#> 1                        0                      0                       0
#> 2                        0                      0                       0
#> 3                        0                      0                       0
#>   samplesMissingFromColData
#>                   <integer>
#> 1                         0
#> 2                         0
#> 3                         0
#> 
#> $samples
#> $samples$overall
#> DataFrame with 1 row and 2 columns
#>   sampleCount samplesInAnyAssay
#>     <integer>         <integer>
#> 1          20                20
#> 
#> $samples$groups
#> DataFrame with 2 rows and 2 columns
#>    groupLevel sampleCount
#>   <character>   <integer>
#> 1        PASC          10
#> 2   Recovered          10
#> 
#> 
#> $warnings
#> character(0)
head(knowledgeNetwork)
#> DataFrame with 6 rows and 30 columns
#>   fromFeatureIdentifier toFeatureIdentifier fromFeatureName toFeatureName
#>             <character>         <character>     <character>   <character>
#> 1                    P1                  P2             CFH            C3
#> 2                    P1                  P3             CFH           CR1
#> 3                    T1                  T2  CFH transcript C3 transcript
#> 4                    T1                  P1  CFH transcript           CFH
#> 5                    T2                  P2   C3 transcript            C3
#> 6                    M1                  P1         Citrate           CFH
#>   fromAssayName toAssayName               edgeType edgeDirection  sourceType
#>     <character> <character>            <character>   <character> <character>
#> 1       protein     protein proteinProteinIntera..    undirected   knowledge
#> 2       protein     protein proteinProteinIntera..    undirected   knowledge
#> 3    transcript  transcript      coexpressionPrior    undirected   knowledge
#> 4    transcript     protein             rnaProtein      directed   knowledge
#> 5    transcript     protein             rnaProtein      directed   knowledge
#> 6    metabolite     protein      proteinMetabolite      directed   knowledge
#>   correlationScope correlationMethod   knowledgeSource   groupName
#>        <character>       <character>       <character> <character>
#> 1               NA                NA CorNettoSynthetic          NA
#> 2               NA                NA CorNettoSynthetic          NA
#> 3               NA                NA CorNettoSynthetic          NA
#> 4               NA                NA CorNettoSynthetic          NA
#> 5               NA                NA CorNettoSynthetic          NA
#> 6               NA                NA CorNettoSynthetic          NA
#>   comparisonName correlationValue group1CorrelationValue group2CorrelationValue
#>      <character>        <numeric>              <numeric>              <numeric>
#> 1             NA               NA                     NA                     NA
#> 2             NA               NA                     NA                     NA
#> 3             NA               NA                     NA                     NA
#> 4             NA               NA                     NA                     NA
#> 5             NA               NA                     NA                     NA
#> 6             NA               NA                     NA                     NA
#>      pValue adjustedPValue group1PValue group2PValue group1AdjustedPValue
#>   <numeric>      <numeric>    <numeric>    <numeric>            <numeric>
#> 1        NA             NA           NA           NA                   NA
#> 2        NA             NA           NA           NA                   NA
#> 3        NA             NA           NA           NA                   NA
#> 4        NA             NA           NA           NA                   NA
#> 5        NA             NA           NA           NA                   NA
#> 6        NA             NA           NA           NA                   NA
#>   group2AdjustedPValue zScoreDifference sampleCount group1SampleCount
#>              <numeric>        <numeric>   <numeric>         <numeric>
#> 1                   NA               NA          NA                NA
#> 2                   NA               NA          NA                NA
#> 3                   NA               NA          NA                NA
#> 4                   NA               NA          NA                NA
#> 5                   NA               NA          NA                NA
#> 6                   NA               NA          NA                NA
#>   group2SampleCount edgeWeight evidenceScore isDirected
#>           <numeric>  <numeric>     <numeric>  <logical>
#> 1                NA       0.95          0.95      FALSE
#> 2                NA       0.92          0.92      FALSE
#> 3                NA       0.80          0.80      FALSE
#> 4                NA       1.00          1.00       TRUE
#> 5                NA       1.00          1.00       TRUE
#> 6                NA       0.88          0.88       TRUE
seedNodes
#> [1] "P1" "T1" "M1"

The synthetic example contains three assays named protein, transcript, and metabolite. The example prior network also contains a drug-target edge to demonstrate that prior networks can include static knowledge beyond the measured assays. Prior-guided candidate-edge analyses must therefore be given only prior edges whose assay names are present in the analysis object.

measuredAssays <- c("protein", "transcript", "metabolite")
measuredKnowledgeNetwork <- knowledgeNetwork[
    knowledgeNetwork$fromAssayName %in% measuredAssays &
        knowledgeNetwork$toAssayName %in% measuredAssays,
    ,
    drop = FALSE
]

Dense group-specific correlations

createCorrelationNetwork() computes all pairwise within-omic correlations for one assay and one group. Dense all-pairs mode is intended for small-to-medium assays; for large assays, prefer prior-guided candidate-edge testing when suitable.

recoveredProteinNetwork <- createCorrelationNetwork(
    analysisData = analysisData,
    assayName = "protein",
    groupColumn = "clinicalGroup",
    groupLevel = "Recovered",
    correlationMethod = "pearson",
    minimumAbsoluteCorrelation = 0,
    adjustedPValueThreshold = 1,
    pAdjustMethod = "fdr",
    storeResult = FALSE
)

head(recoveredProteinNetwork)
#> DataFrame with 6 rows and 30 columns
#>   fromFeatureIdentifier toFeatureIdentifier fromFeatureName toFeatureName
#>             <character>         <character>     <character>   <character>
#> 1                    P1                  P2             CFH            C3
#> 2                    P1                  P3             CFH           CR1
#> 3                    P2                  P3              C3           CR1
#> 4                    P1                  P4             CFH           CFB
#> 5                    P2                  P4              C3           CFB
#> 6                    P3                  P4             CR1           CFB
#>   fromAssayName toAssayName    edgeType edgeDirection  sourceType
#>     <character> <character> <character>   <character> <character>
#> 1       protein     protein correlation      positive correlation
#> 2       protein     protein correlation      positive correlation
#> 3       protein     protein correlation      positive correlation
#> 4       protein     protein correlation      negative correlation
#> 5       protein     protein correlation      negative correlation
#> 6       protein     protein correlation      negative correlation
#>   correlationScope correlationMethod knowledgeSource   groupName comparisonName
#>        <character>       <character>     <character> <character>    <character>
#> 1       withinOmic           pearson              NA   Recovered             NA
#> 2       withinOmic           pearson              NA   Recovered             NA
#> 3       withinOmic           pearson              NA   Recovered             NA
#> 4       withinOmic           pearson              NA   Recovered             NA
#> 5       withinOmic           pearson              NA   Recovered             NA
#> 6       withinOmic           pearson              NA   Recovered             NA
#>   correlationValue group1CorrelationValue group2CorrelationValue      pValue
#>          <numeric>              <numeric>              <numeric>   <numeric>
#> 1         0.878752                     NA                     NA 0.000814789
#> 2         0.747338                     NA                     NA 0.012972274
#> 3         0.787569                     NA                     NA 0.006833154
#> 4        -0.431744                     NA                     NA 0.212790881
#> 5        -0.509253                     NA                     NA 0.132731940
#> 6        -0.475459                     NA                     NA 0.164878371
#>   adjustedPValue group1PValue group2PValue group1AdjustedPValue
#>        <numeric>    <numeric>    <numeric>            <numeric>
#> 1     0.00226296           NA           NA                   NA
#> 2     0.02162046           NA           NA                   NA
#> 3     0.01366631           NA           NA                   NA
#> 4     0.23643431           NA           NA                   NA
#> 5     0.18961706           NA           NA                   NA
#> 6     0.20609796           NA           NA                   NA
#>   group2AdjustedPValue zScoreDifference sampleCount group1SampleCount
#>              <numeric>        <numeric>   <numeric>         <numeric>
#> 1                   NA               NA          10                NA
#> 2                   NA               NA          10                NA
#> 3                   NA               NA          10                NA
#> 4                   NA               NA          10                NA
#> 5                   NA               NA          10                NA
#> 6                   NA               NA          10                NA
#>   group2SampleCount edgeWeight evidenceScore isDirected
#>           <numeric>  <numeric>     <numeric>  <logical>
#> 1                NA   0.878752            NA      FALSE
#> 2                NA   0.747338            NA      FALSE
#> 3                NA   0.787569            NA      FALSE
#> 4                NA  -0.431744            NA      FALSE
#> 5                NA  -0.509253            NA      FALSE
#> 6                NA  -0.475459            NA      FALSE

Prior-guided sparse differential correlation

testDifferentialCorrelation() can test only feature pairs supported by a prior network when candidateEdgeTable is supplied. This is the sparse mode for differential correlation: it avoids exhaustive cross-omic testing and keeps the dynamic network aligned to interpretable prior knowledge.

priorGuidedDifferentialResults <- testDifferentialCorrelation(
    analysisData = analysisData,
    candidateEdgeTable = measuredKnowledgeNetwork,
    groupColumn = "clinicalGroup",
    groupLevels = c("PASC", "Recovered"),
    minimumAbsoluteCorrelation = 0,
    adjustedPValueThreshold = 1,
    pAdjustMethod = "fdr",
    storeResult = FALSE
)

priorGuidedDifferentialNetwork <- createDifferentialCorrelationNetwork(
    differentialCorrelationTable = priorGuidedDifferentialResults,
    minimumAbsoluteCorrelation = 0
)

c(nrow(priorGuidedDifferentialResults), nrow(priorGuidedDifferentialNetwork))
#> [1] 7 7

Permutation validation

permuteRewiringScores() permutes group labels, recomputes the differential-correlation statistics over a fixed edge universe, and compares each node’s observed rewiring score with the permutation distribution. When both candidateEdgeTable and assayName are omitted, that universe is built from dense within-omic pairs in every assay; cross-omic pairs are not generated.

The fixed edge set is attempted in every permutation. An edge can still be unscored if a permuted group has insufficient pairwise-complete data or a constant feature, and contributingPermutations records the resulting loss. Supplying candidateEdgeTable with all observed-data filters disabled, as below, lets CorNetto fix the candidate universe before inspecting the group labels. The result is marked as a randomization p-value only when every edge and the selected node score are estimable under the permitted allocations. The user is still responsible for prespecifying the candidates and for the exchangeability assumption.

backend <- BiocParallel::SnowParam(workers = 2, type = "SOCK")

rewiringValidation <- permuteRewiringScores(
    analysisData = analysisData,
    candidateEdgeTable = measuredKnowledgeNetwork,
    groupColumn = "clinicalGroup",
    groupLevels = c("PASC", "Recovered"),
    minimumAbsoluteCorrelation = 0,
    adjustedPValueThreshold = NULL,
    pAdjustMethod = "fdr",
    nPermutations = 99,
    seed = 1,
    # Remove the three lines below to run serially:
    verbose = TRUE,
    progressEvery = 50,
    BPPARAM = backend
)
#> Scoring 99 permutations with 2 workers.
#> Completed 50 of 99 permutations (50.5%).
#> Completed 99 of 99 permutations (100.0%).

head(rewiringValidation$rewiringTable)
#> DataFrame with 6 rows and 18 columns
#>          nodeKey nodeIdentifier       nodeName   assayName totalConnections
#>      <character>    <character>    <character> <character>        <integer>
#> 1    protein::P1             P1            CFH     protein                4
#> 2 transcript::T1             T1 CFH transcript  transcript                2
#> 3 transcript::T2             T2  C3 transcript  transcript                2
#> 4 metabolite::M1             M1        Citrate  metabolite                1
#> 5 metabolite::M2             M2      Succinate  metabolite                1
#> 6    protein::P2             P2             C3     protein                3
#>   rawRewiringScore rootMeanSquareRewiringScore   degreeBin degreeMatchedZScore
#>          <numeric>                   <numeric> <character>           <numeric>
#> 1          2.58494                     1.29247       (2,3]                  NA
#> 2          1.83367                     1.29660       (1,2]                  NA
#> 3          2.51915                     1.78131       (1,2]                  NA
#> 4          0.71149                     0.71149       [0,1]                  NA
#> 5          1.40686                     1.40686       [0,1]                  NA
#> 6          2.97487                     1.71754       (1,2]                  NA
#>   permutationTailProbability adjustedPermutationTailProbability nullMeanScore
#>                    <numeric>                          <numeric>     <numeric>
#> 1                       0.06                              0.140      1.476106
#> 2                       0.14                              0.175      1.094358
#> 3                       0.14                              0.175      1.373130
#> 4                       0.26                              0.260      0.554030
#> 5                       0.02                              0.140      0.567395
#> 6                       0.05                              0.140      1.391005
#>   nullSdScore contributingPermutations      scoreColumn nPermutations
#>     <numeric>                <integer>      <character>     <integer>
#> 1    0.570168                       99 rawRewiringScore            99
#> 2    0.578007                       99 rawRewiringScore            99
#> 3    0.792259                       99 rawRewiringScore            99
#> 4    0.431635                       99 rawRewiringScore            99
#> 5    0.353340                       99 rawRewiringScore            99
#> 6    0.736635                       99 rawRewiringScore            99
#>   blockColumn       inferenceStatus
#>   <character>           <character>
#> 1          NA randomization p-value
#> 2          NA randomization p-value
#> 3          NA randomization p-value
#> 4          NA randomization p-value
#> 5          NA randomization p-value
#> 6          NA randomization p-value
rewiringValidation$inferenceStatus
#> [1] "randomization p-value"

For a fully scored node, the smallest tail probability is 1 / (nPermutations + 1), so 99 permutations bottom out at 0.01. contributingPermutations records how many permutations produced a score for each node. Treat the values as conditional rankings whenever inferenceStatus says so.

The call above uses a BiocParallel socket backend, which is the portable choice on Windows. Keep examples and automated checks at two workers or fewer. verbose and progressEvery control progress reporting; BPPARAM selects the backend. A fixed seed gives the same label allocations whatever the worker count, so serial and parallel runs agree numerically.

Differential correlation and rewiring

testDifferentialCorrelation() compares correlation coefficients between two groups. createDifferentialCorrelationNetwork() converts the test results to a weighted network, and calculateRewiringScores() summarizes node-level rewiring. When assayName is omitted, every assay is tested, but pairs are generated only within each assay: protein-protein and transcript-transcript pairs are included, for example, whereas protein-transcript pairs are not.

differentialResults <- testDifferentialCorrelation(
    analysisData = analysisData,
    groupColumn = "clinicalGroup",
    groupLevels = c("PASC", "Recovered"),
    correlationMethod = "pearson",
    minimumAbsoluteCorrelation = 0,
    adjustedPValueThreshold = 1,
    pAdjustMethod = "fdr",
    storeResult = FALSE
)

differentialNetwork <- createDifferentialCorrelationNetwork(
    differentialCorrelationTable = differentialResults,
    minimumAbsoluteCorrelation = 0
)

rewiringScores <- calculateRewiringScores(
    differentialCorrelationNetwork = differentialNetwork,
    storeResult = FALSE
)

head(rewiringScores)
#> DataFrame with 6 rows and 9 columns
#>          nodeKey nodeIdentifier       nodeName   assayName totalConnections
#>      <character>    <character>    <character> <character>        <integer>
#> 1    protein::P3             P3            CR1     protein                4
#> 2    protein::P1             P1            CFH     protein                4
#> 3    protein::P2             P2             C3     protein                4
#> 4    protein::P4             P4            CFB     protein                4
#> 5 transcript::T1             T1 CFH transcript  transcript                4
#> 6 transcript::T3             T3 CR1 transcript  transcript                4
#>   rawRewiringScore rootMeanSquareRewiringScore   degreeBin degreeMatchedZScore
#>          <numeric>                   <numeric> <character>           <numeric>
#> 1          6.18963                     3.09481       (2,3]            0.968730
#> 2          5.71175                     2.85588       (2,3]            0.768908
#> 3          4.81787                     2.40894       (2,3]            0.395134
#> 4          2.79769                     1.39884       (2,3]           -0.449601
#> 5          2.61704                     1.30852       (2,3]           -0.525138
#> 6          2.73210                     1.36605       (2,3]           -0.477028

The three scores are descriptive, not tests. rawRewiringScore is the L2 norm of a node’s incident z-scores and grows with degree. rootMeanSquareRewiringScore divides that by sqrt(degree) and is the one to compare across nodes of different degree. degreeMatchedZScore standardizes within bins of log2(degree + 1), so it is a within-bin ranking aid rather than a z-score against a null model; it is NA for bins holding fewer than five nodes, since a smaller bin cannot produce a meaningful spread. permuteRewiringScores() adds a permutation reference; its inferenceStatus states whether the result meets the computational conditions for randomization inference.

Features are mutually tested only against features in the same assay. In this example, each protein and transcript node therefore has degree four, while each metabolite node has degree three. On a real network with a spread of degrees, the bins do the work they are named for.

Integrated and focused network

combineNetworks() merges static prior knowledge, group-specific correlation layers, and differential-correlation layers. createFocusedNetwork() extracts a seed-centered neighborhood, which is useful for pathway-driven analyses.

integratedNetwork <- combineNetworks(
    knowledgeNetwork = knowledgeNetwork,
    correlationNetwork = recoveredProteinNetwork,
    differentialCorrelationNetwork = list(
        priorGuidedDifferentialNetwork,
        differentialNetwork
    ),
    includeReverseEdges = TRUE
)

focusedNetwork <- createFocusedNetwork(
    networkEdgeTable = integratedNetwork,
    seedNodes = seedNodes,
    neighborhoodOrder = 1
)

cytoscapeTables <- prepareCytoscapeTables(
    networkEdgeTable = focusedNetwork,
    rewiringTable = rewiringScores
)

names(cytoscapeTables)
#> [1] "nodes" "edges"
nrow(cytoscapeTables$nodes)
#> [1] 14
nrow(cytoscapeTables$edges)
#> [1] 96

Graph creation

createNetworkGraph() converts a standardized CorNetto edge table to an igraph object. Plotting can then use standard igraph methods.

A node is one feature in one assay, drawn with an assay-specific colour and shape. An edge is a connection between two features. Node colour could equally encode a rewiring score, a permutation tail probability, or any other node-level column.

displayGraph <- igraph::simplify(
    igraph::as_undirected(createNetworkGraph(focusedNetwork), mode = "collapse")
)

assayStyle <- data.frame(
    assay = c("protein", "transcript", "metabolite"),
    colour = c("#2B8CBE", "#31A354", "#E6842A"),
    shape = c("circle", "square", "csquare")
)
idx <- match(igraph::V(displayGraph)$assayName, assayStyle$assay)

set.seed(1)
plot(
    displayGraph,
    layout = igraph::layout_with_fr(displayGraph),
    vertex.color = assayStyle$colour[idx],
    vertex.shape = assayStyle$shape[idx],
    vertex.label = igraph::V(displayGraph)$nodeName,
    vertex.label.cex = 0.7,
    vertex.label.color = "black",
    vertex.label.family = "sans",
    vertex.frame.color = "white",
    vertex.size = 20,
    edge.color = "grey65",
    main = "Focused CorNetto network around the seed nodes"
)
legend(
    "bottomleft",
    legend = assayStyle$assay,
    pt.bg = assayStyle$colour,
    pch = c(21, 22, 23),
    pt.cex = 1.6,
    bty = "n",
    title = "Assay"
)

c(nodes = igraph::gorder(displayGraph),
  connections = igraph::gsize(displayGraph))
#>       nodes connections 
#>          14          30

The 96 exported edge rows reduce to 30 drawn connections for two reasons, and no edge is filtered out on the way.

First, combineNetworks(includeReverseEdges = TRUE) stores each undirected edge in both directions, which accounts for most of the difference. Reverse copies are added for undirected edges only. This is a workaround rather than a claim about biology: several Cytoscape algorithms require an explicit direction to traverse an edge, while the edges themselves are not inherently directed, as with a correlation or a protein-protein interaction. Storing both orientations lets those algorithms move either way across such an edge. Genuinely directed edges are left untouched.

Second, one feature pair can carry several rows when more than one layer supports it, since prior knowledge, a group-specific correlation and a differential-correlation result are separate rows describing the same pair.

The display graph shows one line per connected pair; the exported tables keep every row.

Export

Use writeNetworkTables() when Cytoscape-ready files are needed. The example writes to a temporary directory.

writtenFiles <- writeNetworkTables(
    networkTables = cytoscapeTables,
    directoryPath = tempdir(),
    prefix = "cornettoExample",
    fileFormat = "tsv"
)

basename(writtenFiles)
#> [1] "cornettoExample_nodes.tsv" "cornettoExample_edges.tsv"

Citation

citation("CorNetto")
#> To cite CorNetto in publications, use:
#> 
#>   Ward B, Balligand J-L, Bamps L, Bommer G, Cani PD, De Greef J, Dewulf
#>   JP, Gatto L, Haufroid V, Kabamba B, Pyr dit Ruys S, Vertommen D,
#>   Yombi JC, Belkhir L, Elens L (2026). Longitudinal multi-omic network
#>   rewiring at the complement-coagulation interface in post-acute
#>   sequelae of COVID-19 (PASC). medRxiv. doi:
#>   10.64898/2026.07.14.26358048.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Article{,
#>     title = {Longitudinal multi-omic network rewiring at the complement-coagulation interface in post-acute sequelae of COVID-19 (PASC)},
#>     author = {Bradley Ward and Jean-Luc Balligand and Laurence Bamps and Guido Bommer and Patrice D. Cani and Julien {De Greef} and Joseph P. Dewulf and Laurent Gatto and Vincent Haufroid and Benoît Kabamba and Sébastien {Pyr dit Ruys} and Didier Vertommen and Jean Cyr Yombi and Leïla Belkhir and Laure Elens},
#>     journal = {medRxiv},
#>     year = {2026},
#>     doi = {10.64898/2026.07.14.26358048},
#>   }
#> 
#> Leïla Belkhir and Laure Elens are joint senior authors.

Session information

sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 26.04.1 LTS
#> 
#> Matrix products: default
#> BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.32.so;  LAPACK version 3.12.0
#> 
#> locale:
#>  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
#>  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
#>  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
#>  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
#>  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
#> 
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] CorNetto_0.99.1  BiocStyle_2.41.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] sass_0.4.10                 generics_0.1.4             
#>  [3] SparseArray_1.13.2          lattice_0.23-1             
#>  [5] digest_0.6.39               magrittr_2.0.5             
#>  [7] evaluate_1.0.5              grid_4.6.1                 
#>  [9] fastmap_1.2.0               jsonlite_2.0.0             
#> [11] Matrix_1.7-6                BiocManager_1.30.27        
#> [13] codetools_0.2-20            jquerylib_0.1.4            
#> [15] abind_1.4-8                 cli_3.6.6                  
#> [17] rlang_1.3.0                 XVector_0.53.0             
#> [19] Biobase_2.73.2              withr_3.0.3                
#> [21] cachem_1.1.0                DelayedArray_0.39.6        
#> [23] yaml_2.3.12                 otel_0.2.0                 
#> [25] BiocBaseUtils_1.15.1        S4Arrays_1.13.0            
#> [27] tools_4.6.1                 parallel_4.6.1             
#> [29] BiocParallel_1.47.0         SummarizedExperiment_1.43.0
#> [31] BiocGenerics_0.59.12        MultiAssayExperiment_1.39.1
#> [33] buildtools_1.0.0            R6_2.6.1                   
#> [35] matrixStats_1.5.0           stats4_4.6.1               
#> [37] lifecycle_1.0.5             Seqinfo_1.3.2              
#> [39] S4Vectors_0.51.10           IRanges_2.47.5             
#> [41] pkgconfig_2.0.3             bslib_0.12.0               
#> [43] xfun_0.60                   GenomicRanges_1.65.4       
#> [45] sys_3.4.3                   MatrixGenerics_1.25.0      
#> [47] knitr_1.52                  htmltools_0.5.9            
#> [49] snow_0.4-4                  igraph_2.3.3               
#> [51] rmarkdown_2.32              maketools_1.3.2            
#> [53] compiler_4.6.1