Function reference
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applySparrowBonferroni()
- Applies Bonferroni Correction to a Sparrow Network Object
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applySparrowFDR()
- Applies FDR Correction to a Sparrow Network Object
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aracne()
- This function applies ARACNE on the data
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arbitrarySparsity()
- This function Builds an arbitary sparse network
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buildConsensus()
- This function builds the Consensus Network from the component network
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c3netWrapper()
- This function wraps c3net implementation
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calcHyperPval()
- Compute Hypergeometric PValue
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compute(<LocalModularity>)
- Find Local Modularity (NQ)
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compute(<Modularity>)
- Find Global Modularity (Q)
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compute(<ModularityDensity>)
- Find Global Modularity (Qds)
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compute(<ModuleQualityMetric>)
- Find Modularity Quality
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computeBICcurve()
- This Function Computes a Network BIC Curve
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computeDriverDistance()
- Compute Graph Distance
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computeDriverDistancePvalue()
- Compute Driver Distance P-Value
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correlation()
- Compute a Correlation Matrix
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covarianceSelection()
- Covariance Selection with Graphical Lasso
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covarianceSelectionBisection()
- Covariance Selection with Bisection Optimization
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covarianceSelectionMBPath()
- Covariance based on Neighborhood Selection
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covarianceSelectionPath()
- Covariance Selection with Path BIC Selection
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deployNetworkSparsity()
- This function deploys arbitary sparsity on a list of networks
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fastlm()
- Fast Linear Modeling
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fastlmbic()
- Fast Linear Modeling BIC
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fdrThres()
- FDR Threshold
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findModules.CFinder()
- Find Modules with CFinder
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findModules.CFinder.once()
- This function tries to get modules from network adjacency matrix using Link communities algorithm.
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findModules.GANXiS()
- Find Modules with GANXiS
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findModules.GANXiS.once()
- Find Modules using GANXIS
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findModules.consensusCluster()
- Finds Consensus Clusters
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findModules.consensusKmeans()
- Finds Moduless With Kmeans Clustering
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findModules.edge_betweenness()
- Find Modules with Network Adjacency
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findModules.edge_betweenness.once()
- Find Modules with Network Adjacency Single iteration
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findModules.fast_greedy()
- Find Modules with iGraph Fast and Greedy algorithm
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findModules.fast_greedy.once()
- Find Modules Fast and Greedy
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findModules.hclust()
- Find Modules with Hierarchical Clustering
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findModules.infomap()
- Find Modules using graph adjacency
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findModules.infomap.once()
- Find Modules using graph adjacency
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findModules.label_prop()
- Wraps Finding Modules with Graph Adjacency
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findModules.label_prop.once()
- Find Modules with Graph Adjacency
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findModules.leading_eigen()
- Find Modules with Leading Eigen Edges
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findModules.leading_eigen.once()
- Find Modules with Leading Eigen Edges
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findModules.linkcommunities()
- Find Modules with Network Adjacency Matrix Using Link Communities Algorithm
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findModules.linkcommunities.once()
- Find Modules with Network Adjacency Matrix Using Link Communities Algorithm
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findModules.louvain()
- Find Modules with Network Adjacency Matrix Using Louvain Clustering
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findModules.louvain.once()
- Find Modules with Network Adjacency Matrix Using Louvain Clustering
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findModules.megena()
- Find Modules with Megena Clustering
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findModules.spinglass()
- Find Modules with Network Adjacency Matrix Using Spinglass Clustering
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findModules.spinglass.once()
- Find Modules with Network Adjacency Matrix Using spinglass Clustering
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findModules.walktrap()
- Find Modules with Network Adjacency Matrix Using Walktrap Clustering
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findModules.walktrap.once()
- Find Modules with Network Adjacency Matrix Using Walktrap Clustering
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fisherEnrichment()
- Fishers Enrichment Analysis
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genie3()
- Run genie3
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installAracne()
- This function applies ARACNE on the data
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lassoAIC()
- Grab AIC Solution for Lasso
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lassoBIC()
- Grab BIC Solution for Lasso
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lassoCV1se()
- Grab CV1se Solution for Lasso Regression
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lassoCVmin()
- Grab CV1min Solution for Lasso Regression
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metaNet-package
- metaNet: Statistical network exploration toolkit
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mpiWrapper()
- Runs Sparrow Regression
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mrnetWrapper()
- Implements mrnet
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multiplot()
- Multiple plot function
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rankConsensus()
- Ranks Consensus networks
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rankConsensus2()
- Ranks Consensus networks with ties.
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rankedEdgeList()
- Rank edges of a coexpression matrix
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regulatorAnalysis.directed()
- Function to Identify Network Regulators from Directed Networks
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regulatorAnalysis.directed_weighted()
- Function to Identify Network Regulators from Directed Weighted Networks
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regulatorAnalysis.undirected()
- Function to Identify Network Regulators from Undirected Networks
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regulatorAnalysis.undirected_weighted()
- Function to Identify Network Regulators from Undirected Weighted Networks
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ridgeAIC()
- Grab AIC Solution for Ridge Regression
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ridgeBIC()
- Grab BIC Solution for Ridge Regression
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ridgeCV1se()
- Grab CV1se Solution for Ridge Regression
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ridgeCVmin()
- Grab CV1min Solution for Ridge Regression
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run.consensus.cluster()
- Runs Consensus Clustering Algorithm
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score.nodes()
- Scores Nodes From Regulator Discovery
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simulateNetworkData()
- Simulate Network Data
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sparrow2Z()
- Runs vbsr with a 2Z cutoff for a gene across a matrix
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sparrowNetwork()
- Runs wraps vbsr across a matrix
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sparrowZ()
- Runs vbsr gene across a matrix
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synGetFiles()
- This function pulls files from a synapse project
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tigress()
- Runs tigress on an expression matrix
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wgcnaSoftThreshold()
- Runs WGCNA
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wgcnaTOM()
- Runs WGCNA TOMsimilarity
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winsorizeData()
- Winsorize a Gene Expression Matrix