This vignette is an applied companion to vignette("CorNetto"), which
introduces the package, motivates it against related Bioconductor software,
and covers installation and the full function reference. Read that one first.
Here the same workflow is run end to end on a small packaged subset of
proteomic, transcriptomic, and metabolomic data from moderate and severe
COVID-19 patients. The subset exists to demonstrate the workflow during
package checks; it is not sized for biological discovery, and its provenance
is documented in inst/scripts/covid-example-data.md. The candidate edges
used below are entirely synthetic. They exercise network import, filtering,
differential correlation, and node scoring, but they do not represent
biological evidence.
Constraining the calculations to an explicit candidate network keeps this example fast and avoids dense all-pairs correlations.
CorNetto accepts prior-knowledge networks from external resources when their columns can be mapped to the package schema. For this runnable example, the package instead supplies three deterministic synthetic networks. This avoids redistributing third-party interaction data and keeps the example independent of database versions and licenses.
The within-assay and cross-assay files contain invented edges between measured features. The decoy file contains invented edges with one or two unmeasured endpoints, so the filtering step below has observable work to do. Edge weights and directions are illustrative only.
readKnowledgeNetwork() function takes a delimited table, optionally remaps
the input column names to a common convention, and standardizes it for downstream
CorNetto functions. The minimum columns required are fromFeatureIdentifier,
toFeatureIdentifier, fromAssayName, and toAssayName. Other columns are
optional but can be useful. The output is a standardized S4Vectors::DataFrame.
combineKnowledgeNetworks() takes these standardized networks and combines their rows while removing duplicate edges (optional).
# Set column mapping
priorColumnMap <- c(
fromFeatureIdentifier = "from",
toFeatureIdentifier = "to",
fromFeatureName = "fromName",
toFeatureName = "toName",
fromAssayName = "fromOmic",
toAssayName = "toOmic",
edgeType = "edgeType",
edgeDirection = "edgeDirection",
evidenceScore = "edgeWeight"
)
# Import the three synthetic edge sets
withinAssayNetwork <- readKnowledgeNetwork(
filePath = file.path(
extdataDir, "priorNetworks", "synthetic_within_assay.csv"
),
columnMapping = priorColumnMap,
knowledgeSource = "CorNetto synthetic within-assay"
)
crossAssayNetwork <- readKnowledgeNetwork(
filePath = file.path(
extdataDir, "priorNetworks", "synthetic_cross_assay.csv"
),
columnMapping = priorColumnMap,
knowledgeSource = "CorNetto synthetic cross-assay"
)
decoyNetwork <- readKnowledgeNetwork(
filePath = file.path(
extdataDir, "priorNetworks", "synthetic_decoy_edges.csv"
),
columnMapping = priorColumnMap,
knowledgeSource = "CorNetto synthetic decoys"
)
# Combine networks
combinedPriorNetwork <- combineKnowledgeNetworks(
withinAssayNetwork,
crossAssayNetwork,
decoyNetwork,
removeDuplicates = TRUE
)Currently CorNetto is able to accept any normalised and fully preprocessed abundance or count matrix relating to proteomics, transcriptomics, or metabolomics.
createAnalysisData() creates a MultiAssayExperiment from these normalized assay objects and sample metadata.
filterSamples() filters the MultiAssayExperiment based on a group column and a range of values.
# Import patient data
patientInformation <- read.csv(file.path(extdataDir,
"covidData",
"patientInformation.csv"))
rownames(patientInformation) <- patientInformation$Visit_ID
# Import omics data
dataList <- list(RNA = read.csv(file.path(extdataDir,
"covidData",
"rnaMatrix.csv"),
row.names = 1),
Protein = read.csv(file.path(extdataDir,
"covidData",
"proteinMatrix.csv"),
row.names = 1),
Metabolite = read.csv(file.path(extdataDir,
"covidData",
"metaboliteMatrix.csv"),
row.names = 1))
# Create analysis data
analysisData <- createAnalysisData(assayList = dataList,
sampleData = patientInformation)
# Filter data for only COVID-19 patients at visit 1
analysisCovid <- filterSamples(analysisData,
groupColumn = "Group",
groupLevels = c("COVID Severe",
"COVID Moderate"))
analysisCovid <- filterSamples(analysisCovid,
groupColumn = "Visit",
groupLevels = "Visit 1")Following data importation, objects can be checked for structural conformity and diagnostic summaries.
validateAnalysisData() checks the CorNetto analysis object for structural conformity, specifically:
validateKnowledgeNetwork() checks the CorNetto prior-knowledge edge table
for structural conformity, specifically:
These functions also run quietly after most CorNetto functions to ensure conformity of output.
# Check analysis data
analysisCovid <- validateAnalysisData(analysisCovid)
#> Analysis data object is structured correctly for CorNetto.
# Check knowledge networks
combinedPriorNetwork <- validateKnowledgeNetwork(combinedPriorNetwork)
#> Knowledge network is structured correctly for CorNetto.summarizeAnalysisData() asks “what does this analysis object look like,
and are there analysis risks?” rather than only asking whether the object
is structurally valid.
summarizeAnalysisData(analysisCovid,
groupColumn = "Group")
#> Warning: Assay `RNA` has 6 zero-variance features.
#> $assays
#> DataFrame with 3 rows and 9 columns
#> assayName featureCount sampleCount missingValueCount missingValueFraction
#> <character> <integer> <integer> <integer> <numeric>
#> 1 RNA 51 24 0 0
#> 2 Protein 63 24 0 0
#> 3 Metabolite 4 24 0 0
#> zeroVarianceFeatureCount allMissingFeatureCount highMissingFeatureCount
#> <integer> <integer> <integer>
#> 1 6 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 24 24
#>
#> $samples$groups
#> DataFrame with 2 rows and 2 columns
#> groupLevel sampleCount
#> <character> <integer>
#> 1 COVID Moderate 12
#> 2 COVID Severe 12
#>
#>
#> $warnings
#> [1] "Assay `RNA` has 6 zero-variance features."The output reports the number of features per assay, the number of samples per assay, and the number of samples in each selected group. In this small packaged example there are 12 moderate COVID-19 samples and 12 severe COVID-19 samples.
Zero-variance and near-zero-variance features should be removed before
correlation analysis. These can be removed with removeZeroVariance = TRUE
and minimumVariance in filterFeatures().
analysisCovid <- filterFeatures(
analysisCovid,
removeZeroVariance = TRUE,
minimumVariance = 0.01
)
summarizeAnalysisData(analysisCovid)
#> $assays
#> DataFrame with 3 rows and 9 columns
#> assayName featureCount sampleCount missingValueCount missingValueFraction
#> <character> <integer> <integer> <integer> <numeric>
#> 1 RNA 44 24 0 0
#> 2 Protein 63 24 0 0
#> 3 Metabolite 4 24 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 24 24
#>
#> $samples$groups
#> NULL
#>
#>
#> $warnings
#> character(0)For the sparse workflow, candidate edges are restricted to edges whose endpoint features remain in the filtered analysis object. The first summary shows that the combined synthetic network includes deliberately unmeasured endpoints. The second summary shows the network after requiring both endpoints to be measured.
measuredNodeKeys <- unlist(
lapply(
names(MultiAssayExperiment::experiments(analysisCovid)),
function(assayName) {
assayObject <- MultiAssayExperiment::experiments(analysisCovid)[[assayName]]
paste(
assayName,
rownames(SummarizedExperiment::assay(assayObject)),
sep = "::"
)
}
),
use.names = FALSE
)
unfilteredPriorSummary <- summarizeKnowledgeNetwork(
combinedPriorNetwork,
analysisCovid,
quiet = TRUE
)
unfilteredPriorSummary$overall
#> DataFrame with 1 row and 8 columns
#> totalEdges uniqueNodes duplicateEdges missingFeatureNameEdges
#> <integer> <integer> <integer> <integer>
#> 1 183 118 0 0
#> measuredAssayEdges unmeasuredAssayEdges measuredFeatureEdges
#> <integer> <integer> <integer>
#> 1 181 2 175
#> unmeasuredFeatureEdges
#> <integer>
#> 1 8
measuredPriorNetwork <- filterNetworkByNodes(
combinedPriorNetwork,
nodes = measuredNodeKeys,
mode = "both",
nodeIdType = "key"
)
measuredPriorSummary <- summarizeKnowledgeNetwork(
measuredPriorNetwork,
analysisCovid,
quiet = TRUE
)
measuredPriorSummary$overall
#> DataFrame with 1 row and 8 columns
#> totalEdges uniqueNodes duplicateEdges missingFeatureNameEdges
#> <integer> <integer> <integer> <integer>
#> 1 175 111 0 0
#> measuredAssayEdges unmeasuredAssayEdges measuredFeatureEdges
#> <integer> <integer> <integer>
#> 1 175 0 175
#> unmeasuredFeatureEdges
#> <integer>
#> 1 0The main function for testing differential correlations between groups is
testDifferentialCorrelation. Here it tests every measured edge in the
synthetic candidate network supplied through candidateEdgeTable. The result
illustrates the software workflow and is not a prior-supported biological
analysis.
The differential correlation result is added to analysisCovid, then
extracted with differentialCorrelationResults(). Alternatively,
testDifferentialCorrelation() can directly return the table with
storeResult = FALSE.
analysisCovid <- testDifferentialCorrelation(
analysisData = analysisCovid,
groupColumn = "Group",
groupLevels = c("COVID Moderate", "COVID Severe"),
candidateEdgeTable = measuredPriorNetwork,
correlationMethod = "pearson",
minimumAbsoluteCorrelation = 0.3,
adjustedPValueThreshold = 0.05,
pAdjustMethod = "fdr",
resultName = "differentialSparseCorrelations"
)
differentialSparseCorrelations <-
differentialCorrelationResults(analysisCovid)[["differentialSparseCorrelations"]]
as.data.frame(differentialSparseCorrelations)
#> fromFeatureIdentifier toFeatureIdentifier fromFeatureName toFeatureName
#> 1 ENSG00000132485 ENSG00000132953 ENSG00000132485 ENSG00000132953
#> 2 ENSG00000088179 ENSG00000091009 ENSG00000088179 ENSG00000091009
#> 3 P49720 P60900 P49720 P60900
#> 4 P25788 P40306 P25788 P40306
#> 5 P40306 O14818 P40306 O14818
#> 6 O14818 P28062 O14818 P28062
#> 7 P28062 Q99436 P28062 Q99436
#> fromAssayName toAssayName edgeType edgeDirection
#> 1 RNA RNA differentialCorrelation <NA>
#> 2 RNA RNA differentialCorrelation <NA>
#> 3 Protein Protein differentialCorrelation <NA>
#> 4 Protein Protein differentialCorrelation <NA>
#> 5 Protein Protein differentialCorrelation <NA>
#> 6 Protein Protein differentialCorrelation <NA>
#> 7 Protein Protein differentialCorrelation <NA>
#> sourceType correlationScope correlationMethod
#> 1 differentialCorrelationTest withinOmic pearson
#> 2 differentialCorrelationTest withinOmic pearson
#> 3 differentialCorrelationTest withinOmic pearson
#> 4 differentialCorrelationTest withinOmic pearson
#> 5 differentialCorrelationTest withinOmic pearson
#> 6 differentialCorrelationTest withinOmic pearson
#> 7 differentialCorrelationTest withinOmic pearson
#> knowledgeSource groupName comparisonName
#> 1 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 2 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 3 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 4 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 5 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 6 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 7 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> correlationValue group1CorrelationValue group2CorrelationValue pValue
#> 1 NA 0.902032305 0.5521418 0.06760191
#> 2 NA 0.864196626 0.2718904 0.02876993
#> 3 NA 0.498555926 0.8801615 0.07861328
#> 4 NA 0.526881781 0.8826141 0.08902387
#> 5 NA 0.754467699 0.9130670 0.23284375
#> 6 NA 0.124513627 0.8523859 0.01562476
#> 7 NA 0.004922372 0.8328467 0.01142136
#> adjustedPValue group1PValue group2PValue group1AdjustedPValue
#> 1 0.5158465 6.017295e-05 6.268702e-02 0.01028957
#> 2 0.3692840 2.883267e-04 3.926101e-01 0.02465193
#> 3 0.5158465 9.898339e-02 1.586563e-04 0.52306645
#> 4 0.5158465 7.839537e-02 1.436856e-04 0.47877174
#> 5 0.7508336 4.573930e-03 3.374331e-05 0.11508572
#> 6 0.3095907 6.998262e-01 4.284883e-04 0.94210602
#> 7 0.3017165 9.878867e-01 7.706156e-04 0.99046955
#> group2AdjustedPValue zScoreDifference sampleCount group1SampleCount
#> 1 0.272940781 -1.827651 NA 12
#> 2 0.685064612 -2.186625 NA 12
#> 3 0.009043412 1.758789 NA 12
#> 4 0.009043412 1.700569 NA 12
#> 5 0.005770107 1.193065 NA 12
#> 6 0.018317873 2.417565 NA 12
#> 7 0.026355053 2.529536 NA 12
#> group2SampleCount edgeWeight evidenceScore isDirected
#> 1 12 NA 0.90 FALSE
#> 2 12 NA 0.65 FALSE
#> 3 12 NA 0.65 FALSE
#> 4 12 NA 0.95 FALSE
#> 5 12 NA 0.55 FALSE
#> 6 12 NA 0.60 FALSE
#> 7 12 NA 0.65 FALSEThe next step builds a differential correlation network from the
differentialSparseCorrelations table. The function returns the network
directly unless storeResult = TRUE, in which case analysisData and
resultName are supplied.
Here the resulting network is not filtered further. In a real analysis,
minimumAbsoluteCorrelation and differenceAdjustedPValueThreshold can
be tightened to remove weakly correlated or non-significant edges.
analysisCovid <- createDifferentialCorrelationNetwork(
differentialCorrelationTable = differentialSparseCorrelations,
minimumAbsoluteCorrelation = 0,
edgeWeightMethod = "signedZScore",
analysisData = analysisCovid,
resultName = "differentialSparseNetwork",
storeResult = TRUE)
differentialSparseNetwork <-
differentialCorrelationNetworks(analysisCovid)[["differentialSparseNetwork"]]
as.data.frame(differentialSparseNetwork)
#> fromFeatureIdentifier toFeatureIdentifier fromFeatureName toFeatureName
#> 1 ENSG00000132485 ENSG00000132953 ENSG00000132485 ENSG00000132953
#> 2 ENSG00000088179 ENSG00000091009 ENSG00000088179 ENSG00000091009
#> 3 P49720 P60900 P49720 P60900
#> 4 P25788 P40306 P25788 P40306
#> 5 P40306 O14818 P40306 O14818
#> 6 O14818 P28062 O14818 P28062
#> 7 P28062 Q99436 P28062 Q99436
#> fromAssayName toAssayName edgeType edgeDirection
#> 1 RNA RNA differentialCorrelation strengthenedPositive
#> 2 RNA RNA differentialCorrelation strengthenedPositive
#> 3 Protein Protein differentialCorrelation weakenedPositive
#> 4 Protein Protein differentialCorrelation weakenedPositive
#> 5 Protein Protein differentialCorrelation weakenedPositive
#> 6 Protein Protein differentialCorrelation weakenedPositive
#> 7 Protein Protein differentialCorrelation weakenedPositive
#> sourceType correlationScope correlationMethod
#> 1 differentialCorrelation withinOmic pearson
#> 2 differentialCorrelation withinOmic pearson
#> 3 differentialCorrelation withinOmic pearson
#> 4 differentialCorrelation withinOmic pearson
#> 5 differentialCorrelation withinOmic pearson
#> 6 differentialCorrelation withinOmic pearson
#> 7 differentialCorrelation withinOmic pearson
#> knowledgeSource groupName comparisonName
#> 1 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 2 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 3 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 4 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 5 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 6 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> 7 CorNetto synthetic within-assay <NA> COVID Moderate_vs_COVID Severe
#> correlationValue group1CorrelationValue group2CorrelationValue pValue
#> 1 NA 0.902032305 0.5521418 0.06760191
#> 2 NA 0.864196626 0.2718904 0.02876993
#> 3 NA 0.498555926 0.8801615 0.07861328
#> 4 NA 0.526881781 0.8826141 0.08902387
#> 5 NA 0.754467699 0.9130670 0.23284375
#> 6 NA 0.124513627 0.8523859 0.01562476
#> 7 NA 0.004922372 0.8328467 0.01142136
#> adjustedPValue group1PValue group2PValue group1AdjustedPValue
#> 1 0.5158465 6.017295e-05 6.268702e-02 0.01028957
#> 2 0.3692840 2.883267e-04 3.926101e-01 0.02465193
#> 3 0.5158465 9.898339e-02 1.586563e-04 0.52306645
#> 4 0.5158465 7.839537e-02 1.436856e-04 0.47877174
#> 5 0.7508336 4.573930e-03 3.374331e-05 0.11508572
#> 6 0.3095907 6.998262e-01 4.284883e-04 0.94210602
#> 7 0.3017165 9.878867e-01 7.706156e-04 0.99046955
#> group2AdjustedPValue zScoreDifference sampleCount group1SampleCount
#> 1 0.272940781 -1.827651 NA 12
#> 2 0.685064612 -2.186625 NA 12
#> 3 0.009043412 1.758789 NA 12
#> 4 0.009043412 1.700569 NA 12
#> 5 0.005770107 1.193065 NA 12
#> 6 0.018317873 2.417565 NA 12
#> 7 0.026355053 2.529536 NA 12
#> group2SampleCount edgeWeight evidenceScore isDirected
#> 1 12 -1.827651 0.90 FALSE
#> 2 12 -2.186625 0.65 FALSE
#> 3 12 1.758789 0.65 FALSE
#> 4 12 1.700569 0.95 FALSE
#> 5 12 1.193065 0.55 FALSE
#> 6 12 2.417565 0.60 FALSE
#> 7 12 2.529536 0.65 FALSERewiring scores summarize the amount of differential-correlation signal
incident on each node in the differential network. Here the rewiring table
is stored in analysisCovid and extracted with rewiringResults().
analysisCovid <- calculateRewiringScores(
differentialCorrelationNetwork = differentialSparseNetwork,
analysisData = analysisCovid,
resultName = "differentialSparseRewiring",
storeResult = TRUE
)
rewiringScores <- rewiringResults(analysisCovid)[["differentialSparseRewiring"]]
rewiringScores <- rewiringScores[
order(rewiringScores$rootMeanSquareRewiringScore, decreasing = TRUE),
]
head(rewiringScores, 20)
#> DataFrame with 11 rows and 9 columns
#> nodeKey nodeIdentifier nodeName assayName
#> <character> <character> <character> <character>
#> 1 Protein::Q99436 Q99436 Q99436 Protein
#> 2 Protein::P28062 P28062 P28062 Protein
#> 3 RNA::ENSG00000088179 ENSG00000088179 ENSG00000088179 RNA
#> 4 RNA::ENSG00000091009 ENSG00000091009 ENSG00000091009 RNA
#> 5 Protein::O14818 O14818 O14818 Protein
#> 6 RNA::ENSG00000132485 ENSG00000132485 ENSG00000132485 RNA
#> 7 RNA::ENSG00000132953 ENSG00000132953 ENSG00000132953 RNA
#> 8 Protein::P49720 P49720 P49720 Protein
#> 9 Protein::P60900 P60900 P60900 Protein
#> 10 Protein::P25788 P25788 P25788 Protein
#> 11 Protein::P40306 P40306 P40306 Protein
#> totalConnections rawRewiringScore rootMeanSquareRewiringScore degreeBin
#> <integer> <numeric> <numeric> <character>
#> 1 1 2.52954 2.52954 [0,1]
#> 2 2 3.49902 2.47418 (1,2]
#> 3 1 2.18662 2.18662 [0,1]
#> 4 1 2.18662 2.18662 [0,1]
#> 5 2 2.69593 1.90631 (1,2]
#> 6 1 1.82765 1.82765 [0,1]
#> 7 1 1.82765 1.82765 [0,1]
#> 8 1 1.75879 1.75879 [0,1]
#> 9 1 1.75879 1.75879 [0,1]
#> 10 1 1.70057 1.70057 [0,1]
#> 11 2 2.07734 1.46890 (1,2]
#> degreeMatchedZScore
#> <numeric>
#> 1 1.889753
#> 2 NA
#> 3 0.727403
#> 4 0.727403
#> 5 NA
#> 6 -0.489392
#> 7 -0.489392
#> 8 -0.722810
#> 9 -0.722810
#> 10 -0.920155
#> 11 NApermuteRewiringScores() permutes the supplied group labels, reruns the
differential correlation testing, rebuilds the networks, and calculates
node-level permutation tail probabilities. This example retains the observed
correlation and adjusted-p filters used above, so its output is a conditional
ranking, not a randomization p-value. This vignette uses 99 Monte Carlo
permutations so that package checks run quickly. Assignments are sampled
independently and can repeat; exact enumeration is not implemented.
analysisCovid <- permuteRewiringScores(
analysisData = analysisCovid,
groupColumn = "Group",
groupLevels = c("COVID Moderate", "COVID Severe"),
candidateEdgeTable = measuredPriorNetwork,
correlationMethod = "pearson",
minimumAbsoluteCorrelation = 0.3,
adjustedPValueThreshold = 0.05,
pAdjustMethod = "fdr",
edgeWeightMethod = "signedZScore",
scoreColumn = "rawRewiringScore",
nPermutations = 99,
seed = 1,
keepPermutationScores = TRUE,
resultName = "differentialSparseRewiringPermutation",
storeResult = TRUE)
#> Warning: Observed-data edge filtering is active; permutation tail probabilities
#> are conditional rankings.
rewiringPermutation <-
validationResults(analysisCovid)[["differentialSparseRewiringPermutation"]]
rewiringPermutation$inferenceStatus
#> [1] "conditional ranking"
rankedRewiring <- rewiringPermutation$rewiringTable
rankedRewiring <- rankedRewiring[
order(
rankedRewiring$adjustedPermutationTailProbability,
-rankedRewiring$rawRewiringScore,
na.last = TRUE
),
]
head(rankedRewiring, 20)
#> DataFrame with 11 rows and 18 columns
#> nodeKey nodeIdentifier nodeName assayName
#> <character> <character> <character> <character>
#> 1 Protein::P28062 P28062 P28062 Protein
#> 2 Protein::Q99436 Q99436 Q99436 Protein
#> 3 RNA::ENSG00000088179 ENSG00000088179 ENSG00000088179 RNA
#> 4 RNA::ENSG00000091009 ENSG00000091009 ENSG00000091009 RNA
#> 5 Protein::O14818 O14818 O14818 Protein
#> 6 RNA::ENSG00000132485 ENSG00000132485 ENSG00000132485 RNA
#> 7 RNA::ENSG00000132953 ENSG00000132953 ENSG00000132953 RNA
#> 8 Protein::P49720 P49720 P49720 Protein
#> 9 Protein::P60900 P60900 P60900 Protein
#> 10 Protein::P25788 P25788 P25788 Protein
#> 11 Protein::P40306 P40306 P40306 Protein
#> totalConnections rawRewiringScore rootMeanSquareRewiringScore degreeBin
#> <integer> <numeric> <numeric> <character>
#> 1 2 3.49902 2.47418 (1,2]
#> 2 1 2.52954 2.52954 [0,1]
#> 3 1 2.18662 2.18662 [0,1]
#> 4 1 2.18662 2.18662 [0,1]
#> 5 2 2.69593 1.90631 (1,2]
#> 6 1 1.82765 1.82765 [0,1]
#> 7 1 1.82765 1.82765 [0,1]
#> 8 1 1.75879 1.75879 [0,1]
#> 9 1 1.75879 1.75879 [0,1]
#> 10 1 1.70057 1.70057 [0,1]
#> 11 2 2.07734 1.46890 (1,2]
#> degreeMatchedZScore permutationTailProbability
#> <numeric> <numeric>
#> 1 NA 0.06
#> 2 1.889753 0.06
#> 3 0.727403 0.04
#> 4 0.727403 0.04
#> 5 NA 0.17
#> 6 -0.489392 0.19
#> 7 -0.489392 0.19
#> 8 -0.722810 0.20
#> 9 -0.722810 0.20
#> 10 -0.920155 0.28
#> 11 NA 0.33
#> adjustedPermutationTailProbability nullMeanScore nullSdScore
#> <numeric> <numeric> <numeric>
#> 1 0.165000 1.582086 1.035090
#> 2 0.165000 1.060738 0.841214
#> 3 0.165000 0.852394 0.641887
#> 4 0.165000 0.852394 0.641887
#> 5 0.244444 1.650041 0.947227
#> 6 0.244444 1.110976 0.706074
#> 7 0.244444 1.110976 0.706074
#> 8 0.244444 0.982580 0.766584
#> 9 0.244444 0.982580 0.766584
#> 10 0.308000 1.315043 0.960308
#> 11 0.330000 1.780760 1.165209
#> contributingPermutations scoreColumn nPermutations blockColumn
#> <integer> <character> <integer> <character>
#> 1 99 rawRewiringScore 99 NA
#> 2 99 rawRewiringScore 99 NA
#> 3 99 rawRewiringScore 99 NA
#> 4 99 rawRewiringScore 99 NA
#> 5 99 rawRewiringScore 99 NA
#> 6 99 rawRewiringScore 99 NA
#> 7 99 rawRewiringScore 99 NA
#> 8 99 rawRewiringScore 99 NA
#> 9 99 rawRewiringScore 99 NA
#> 10 99 rawRewiringScore 99 NA
#> 11 99 rawRewiringScore 99 NA
#> inferenceStatus
#> <character>
#> 1 conditional ranking
#> 2 conditional ranking
#> 3 conditional ranking
#> 4 conditional ranking
#> 5 conditional ranking
#> 6 conditional ranking
#> 7 conditional ranking
#> 8 conditional ranking
#> 9 conditional ranking
#> 10 conditional ranking
#> 11 conditional rankingOnce results are generated, the network and node rewiring scores can be
supplied to prepareCytoscapeTables() to create tables that can be
imported into Cytoscape for further network analysis.
writeNetworkTables() writes the node and edge tables to a specified
directory. The vignette writes to tempdir() so package checks do not
write into the user’s working directory.
cytoscapeTables <- prepareCytoscapeTables(networkEdgeTable = differentialSparseNetwork,
rewiringTable = rankedRewiring)
writeNetworkTables(cytoscapeTables,
directoryPath = file.path(tempdir(),
"cornettoCovidSeverityNetwork"),
prefix = "covidModerateVsSevere",
fileFormat = "csv")sessionInfo()
#> R version 4.6.1 (2026-06-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
#> LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [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: America/New_York
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
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#>
#> loaded via a namespace (and not attached):
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#> [43] pkgconfig_2.0.3 bslib_0.12.0
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