Function reference • netZooR

Main functions

main functions to run each algorithm

pandaPy(): Run Python implementation PANDA in R

createCondorObject(): Create list amenable to analysis using condor package.

lioness(): Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)

alpaca(): Main ALPACA function

sambar(): Main SAMBAR function.

monster(): MOdeling Network State Transitions from Expression and Regulatory data (MONSTER)

otter(): Run OTTER in R

cobra(): Run COBRA in R

puma(): PANDA using microRNA associations

spider(): Seeding PANDA Interactions to Derive Epigenetic Regulation

tiger(): TIGER main function

runEgret(): Run EGRET in R

dragon(): Run DRAGON in R.

craneBipartite(): Pertrubs the bipartite network with fixed node strength

PANDA

All functions of PANDA

pandaPy(): Run Python implementation PANDA in R

pandaToCondorObject(): Turn PANDA network into a CONDOR object

pandaToAlpaca(): Use two PANDA network to generate an ALPACA result

pandaDiffEdges(): Identify differential edges in two PANDA networks

visPandaInCytoscape(): Plot PANDA network in Cytoscape

createPandaStyle(): Create a Cytoscape visual style for PANDA network

CONDOR

All functions of CONDOR

createCondorObject(): Create list amenable to analysis using condor package.

condorCluster(): Main clustering function for condor.

condorCoreEnrich(): Compare qscore distribution of a subset of nodes to all other nodes.

condorCreateObject(): creates condor object

condorMatrixModularity(): Iteratively maximize bipartite modularity.

condorModularityMax(): Iteratively maximize bipartite modularity.

condorPlotCommunities(): Plot adjacency matrix with links grouped and colored by community

condorPlotHeatmap(): Plot weighted adjacency matrix with links grouped by community

condorQscore(): Calculate Qscore for all nodes

condorRun(): Run CONDOR clustering

LIONESS

All functions of LIONESS

lionessPy(): Run python implementation of LIONESS

lioness(): Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)

ALPACA

All functions of ALPACA

alpaca(): Main ALPACA function

alpacaCommunityStructureRotation(): Comparing node community membership between two networks

alpacaComputeDWBMmatmScale(): Differential modularity matrix

alpacaComputeDifferentialScoreFromDWBM(): Compute Differential modularity score from differential modularity matrix

alpacaComputeWBMmat(): Compute modularity matrix for weighted bipartite network

alpacaCrane(): Find the robust nodes in ALPACA community using CRANE

alpacaDeltaZAnalysis(): Edge subtraction method (CONDOR optimizaton)

alpacaDeltaZAnalysisLouvain(): Edge subtraction method (Louvain optimizaton)

alpacaExtractTopGenes(): Extract core target genes in differential modules

alpacaGOtabtogenes(): The top GO term associated genes in each module

alpacaGenLouvain(): Generalized Louvain optimization

alpacaGetMember(): get the member vector from alpaca object

alpacaGoToGenes(): Map GO terms to gene symbols

alpacaListToGo(): GO term enrichment for a list of gene sets

alpacaMetaNetwork(): Create alpacaMetaNetwork for Louvain optimization

alpacaNodeToGene(): Remove tags from gene names

alpacaObjectToDfList(): Converts alpaca output into list of data frames

alpacaRotationAnalysis(): Community comparison method (CONDOR optimizaton)

alpacaRotationAnalysisLouvain(): Community comparison method (CONDOR optimizaton)

alpacaSimulateNetwork(): Simulated networks

alpacaTestNodeRank(): Enrichment in ranked list

alpacaTidyConfig(): Renumbering community membership vector

alpacaTopEnsembltoTopSym(): Translating gene identifiers to gene symbols

alpacaWBMlouvain(): Generalized Louvain method for bipartite networks

SAMBAR

All functions of SAMBAR

sambar(): Main SAMBAR function.

sambarConvertgmt(): Convert .gmt files into a binary matrix.

sambarCorgenelength(): Normalize gene mutation scores by gene length.

sambarDesparsify(): De-sparsify gene-level mutation scores into gene set-level mutation scores.

MONSTER

All functions of MONSTER

monster(): MOdeling Network State Transitions from Expression and Regulatory data (MONSTER)

monsterBereFull(): Bipartite Edge Reconstruction from Expression data (composite method with direct/indirect)

monsterCalculateTmPValues(): Calculate p-values for a tranformation matrix

monsterCheckDataType(): Checks that data is something MONSTER can handle

monsterGetTm(): monsterGetTm

monsterHclHeatmapPlot(): Transformation matrix plot

monsterMonsterNI(): Bipartite Edge Reconstruction from Expression data

monsterPlotMonsterAnalysis(): monsterPlotMonsterAnalysis

monsterPrintMonsterAnalysis(): monsterPrintMonsterAnalysis

monsterRes: MONSTER results from example cell-cycle yeast transition

monsterTransformationMatrix(): Bi-partite network analysis tools

monsterTransitionNetworkPlot(): This function uses igraph to plot the transition matrix (directed graph) as a network. The edges in the network should be read as A 'positively/negatively contributes to' the targeting of B in the target state.

monsterTransitionPCAPlot(): Principal Components plot of transformation matrix

monsterdTFIPlot(): This function plots the Off diagonal mass of an observed Transition Matrix compared to a set of null TMs

OTTER

OTTER functions

otter(): Run OTTER in R

TIGER

TIGER functions

tiger(): TIGER main function

TIGER_expr: TIGER example expression matrix

TIGER_prior: TIGER example prior network

adj2regulon(): Convert bipartite adjacency to regulon

priorPp(): Filter low confident edge signs in the prior network using GeneNet

COBRA

COBRA functions

cobra(): Run COBRA in R

CRANE

CRANE functions

craneBipartite(): Pertrubs the bipartite network with fixed node strength

craneUnipartite(): Pertrubs the unipartite network with fixed node strength from adjacency matrix

elistToAdjMat(): Converts edge list to adjacency matrix

elistSort(): Sorts the edge list based on first two columns in alphabetical order

elistRemoveTags(): undo elistAddTags

isElist(): Check if data frame is an edge list

adjMatToElist(): converts adjacency matrix to edge list

el2adj(): Convert bipartite edge list to adjacency mat

el2regulon(): Convert a bipartite edgelist to regulon

elistIsEdgeOrderEqual(): check if first two columns are identical

elistAddTags(): Adds "_A" to first column and "_B" to second column

adj2el(): Convert a bipartite adjacency matrix to an edgelist

jutterDegree(): CRANE Beta perturbation function. This function will add noice to the node strength sequence.

DRAGON

DRAGON functions

dragon(): Run DRAGON in R.

SPIDER

SPIDER functions

spider(): Seeding PANDA Interactions to Derive Epigenetic Regulation

degreeAdjust(): Function to adjust the degree so that the hub nodes are not penalized in z-score transformation

PUMA

PUMA functions

puma(): PANDA using microRNA associations

EGRET

EGRET functions

runEgret(): Run EGRET in R

BLOBFISH

BLOBFISH functions

RunBLOBFISH(): Given a set of genes of interest, full bipartite networks with scores (one network for each sample), a significance cutoff for statistical testing, and a hop constraint, BLOBFISH finds a subnetwork of significant edges connecting the genes.

GenerateNullPANDADistribution(): Generate a null distribution of edge scores for PANDA-like networks; that is, the set of edges where (1) the TF does not have a binding motif in the gene region, (2) the TF does not form a complex with any other TF that has a binding motif in the gene region, and (3) the genes regulated by the TF are not coexpressed with the gene in question. We obtain this by inputting an empty prior and an identity coexpression matrix.

BuildSubnetwork(): Find the subnetwork of significant edges connecting the genes.

CalculatePValues(): Calculate p-values for all edges in the network using a Wilcoxon two-sample test for each edge.

FindSignificantEdgesForHop(): Find the subnetwork of significant edges n / 2 hops away from each gene.

SignificantBreadthFirstSearch(): Find all significant edges adjacent to the starting nodes, excluding the nodes specified.

FindConnectionsForAllHopCounts(): For all hop counts up to the maximum, find subnetworks connecting each pair of genes by exactly that number of hops. For instance, find each

PlotNetwork(): Plot the networks, using different colors for transcription factors, genes of interest, and additional genes.

YARN

YARN functions

checkMisAnnotation(): Check for wrong annotation of a sample using classical MDS and control genes.

checkTissuesToMerge(): Check tissues to merge based on gene expression profile

filterGenes(): Filter specific genes

filterLowGenes(): Filter genes that have less than a minimum threshold CPM for a given group/tissue

normalizeTissueAware(): Normalize in a tissue aware context

annotateFromBiomart(): Annotate your Expression Set with biomaRt

downloadGTEx(): Download GTEx files and turn them into ExpressionSet object

extractMatrix(): Extract the appropriate matrix

filterMissingGenes(): Filter genes not expressed in any sample

filterSamples(): Filter samples

plotCMDS(): Plot classical MDS of dataset

plotDensity(): Density plots of columns in a matrix

plotHeatmap(): Plot heatmap of most variable genes

qsmooth(): Quantile shrinkage normalization

qstats(): Compute quantile statistics

Plotting functions

function to plot the networks and community structures.

createPandaStyle(): Create a Cytoscape visual style for PANDA network

condorPlotCommunities(): Plot adjacency matrix with links grouped and colored by community

condorPlotHeatmap(): Plot weighted adjacency matrix with links grouped by community

Helper functions

miscellaneous function

sourcePPI(): Source the Protein-Protein interaction in STRING database

pandaToCondorObject(): Turn PANDA network into a CONDOR object

pandaToAlpaca(): Use two PANDA network to generate an ALPACA result

Data

Input example data files

yeast: Toy data derived from three gene expression datasets and a mapping from transcription factors to genes.

small1976: Pollinator-plant interactions

mut.ucec mut.ucec: Example of mutation data

exon.size exon.size genes: Gene length

genes: Example of a gene list

bladder: Bladder RNA-seq data from the GTEx consortium

skin: Skin RNA-seq data from the GTEx consortium