# A Menagerie of Methods for the Inference and Analysis of Gene Regulatory Networks

## Documentation for package ‘netZooR’ version 1.14.1

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

A B C D E F G I J L M N O P Q R S T Y

## -- A --

adj2el | Convert a bipartite adjacency matrix to an edgelist
adj2regulon | Convert bipartite adjacency to regulon
adjMatToElist | converts adjacency matrix to edge list
alpaca | Main ALPACA function
alpacaCommunityStructureRotation | Comparing node community membership between two networks
alpacaComputeDifferentialScoreFromDWBM | Compute Differential modularity score from differential modularity matrix
alpacaComputeDWBMmatmScale | 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
alpacaGenLouvain | Generalized Louvain optimization
alpacaGetMember | get the member vector from alpaca object
alpacaGOtabtogenes | The top GO term associated genes in each module
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
annotateFromBiomart | Annotate your Expression Set with biomaRt

## -- B --

bladder | Bladder dataset
BuildSubnetwork | Find the subnetwork of significant edges connecting the genes.

## -- C --

CalculatePValues | Calculate p-values for all edges in the network using a Wilcoxon two-sample test for each edge.
checkMisAnnotation | Check for wrong annotation of a sample using classical MDS and control genes.
checkTissuesToMerge | Check tissues to merge based on gene expression profile
cobra | Run COBRA in R
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
craneBipartite | Pertrubs the bipartite network with fixed node strength
craneUnipartite | Pertrubs the unipartite network with fixed node strength from adjacency matrix
createCondorObject | Create list amenable to analysis using 'condor' package.

## -- D --

degreeAdjust | Function to adjust the degree so that the hub nodes are not penalized in z-score transformation
domonster | MONSTER quick-start with pre-made regulatory networks
downloadGTEx | Download GTEx files and turn them into ExpressionSet object
dragon | Run DRAGON in R.

## -- E --

el2adj | Convert bipartite edge list to adjacency mat
el2regulon | Convert a bipartite edgelist to regulon
elistAddTags | Adds "_A" to first column and "_B" to second column
elistIsEdgeOrderEqual | check if first two columns are identical
elistRemoveTags | undo elistAddTags
elistSort | Sorts the edge list based on first two columns in alphabetical order
elistToAdjMat | Converts edge list to adjacency matrix
exon.size | Gene length
extractMatrix | Extract the appropriate matrix

## -- F --

filterGenes | Filter specific genes
filterLowGenes | Filter genes that have less than a minimum threshold CPM for a given group/tissue
filterMissingGenes | Filter genes not expressed in any sample
filterSamples | Filter samples
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
FindSignificantEdgesForHop | Find the subnetwork of significant edges n / 2 hops away from each gene.

## -- G --

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.
genes | Example of a gene list
gsea_categorical | Function to run GSEA for a categorical phenotype
gsea_numeric | Function to run GSEA for a numeric phenotype

## -- I --

isElist | Check if data frame is an edge list

## -- J --

jutterDegree | CRANE Beta perturbation function. This function will add noice to the node strength sequence.

## -- L --

lioness | Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)
lionessPy | Run python implementation of LIONESS

## -- M --

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
monsterCalculateTmStats | Calculate statistics for a tranformation matrix
monsterCheckDataType | Checks that data is something MONSTER can handle
monsterdTFIPlot | This function plots the Off diagonal mass of an observed Transition Matrix compared to a set of null TMs
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
mut.ucec | Example of mutation data

## -- N --

normalizeTissueAware | Normalize in a tissue aware context

## -- O --

otter | Run OTTER in R

## -- P --

pandaDiffEdges | Identify differential edges in two PANDA networks
pandaPy | Run Python implementation PANDA in R
pandaToAlpaca | Use two PANDA network to generate an ALPACA result
pandaToCondorObject | Turn PANDA network into a CONDOR object
plotCMDS | Plot classical MDS of dataset
plotDensity | Density plots of columns in a matrix
plotHeatmap | Plot heatmap of most variable genes
PlotNetwork | Plot the networks, using different colors for transcription factors, genes of interest, and additional genes.
priorPp | Filter low confident edge signs in the prior network using GeneNet
puma | PANDA using microRNA associations

## -- Q --

qsmooth | Quantile shrinkage normalization
qstats | Compute quantile statistics

## -- R --

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.
runEgret | Run EGRET in R

## -- S --

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.
seahorse | Run SEAHORSE in R Author(s): Enakshi Saha Description: SEAHORSE computes gene-gene coexpression matrix, associations between a set of given phenotypes and each gene, performs gene set enrichment analysis (GSEA) for each phenotype, using the measures of association. GSEA is performed through R package "fgsea". The measures of association for a numerical phenotype is Pearson correlation and for a categorical phenotype is the p-value of an ANOVA test
SignificantBreadthFirstSearch | Find all significant edges adjacent to the starting nodes, excluding the nodes specified.
skin | Skin dataset
small1976 | Pollinator-plant interactions
sourcePPI | Source the Protein-Protein interaction in STRING database
spider | Seeding PANDA Interactions to Derive Epigenetic Regulation

## -- T --

tiger | TIGER main function
TIGER_expr | TIGER example expression matrix
TIGER_prior | TIGER example prior network

## -- Y --

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