{ "_id": "6ac4e078f511ae58c09a297a", "Package": "netZooR", "Type": "Package", "Title": "A menagerie of methods for the inference and analysis of gene\nregulatory networks", "Version": "1.17.0", "Date": "2025-07-31", "Authors@R": "c(person(\"Tara\", \"Eicher\",\nemail = \"teicher@hsph.harvard.edu\", role = c(\"aut\"), comment = c(ORCID = \"0000-0003-1809-4458\")),\nperson(\"Marouen\", \"Ben Guebila\",\nemail = \"marouen.b.guebila@gmail.com\", role = c(\"aut\", \"cre\"), comment = c(ORCID = \"0000-0001-5934-966X\")),\nperson(\"Tian\", \"Wang\",\nemail = \"tian.wang@bc.edu\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-2767-3243\")),\nperson(\"John\", \"Platig\",\nemail=\"john.platig@channing.harvard.edu\", role=\"aut\"),\nperson(\"Marieke\", \"Kuijjer\",\nemail = \"marieke.kuijjer@ncmm.uio.no\", role = \"aut\", comment = c(ORCID = \"0000-0001-6280-3130\")),\nperson(\"Megha\", \"Padi\",\nemail = \"mpadi@email.arizona.edu\", role = \"aut\", comment = c(ORCID = \"0000-0002-3446-4562\")),\nperson(\"Rebekka\", \"Burkholz\",\nemail = \"rburkholz@hsph.harvard.edu\",role = \"aut\"),\nperson(\"Des\", \"Weighill\",\nemail = \"\",role = \"aut\", comment = c(ORCID = \"0000-0003-4979-5871\")),\nperson(\"Chen\", \"Chen\",\nemail = \"\",role = \"aut\", comment = c(ORCID = \"0000-0002-8042-7201\")),\nperson(\"Kate\", \"Shutta\",\nemail = \"\",role = \"aut\", comment = c(ORCID = \"0000-0003-0402-3771\")))", "Description": "netZooR unifies the implementations of several Network Zoo\nmethods (netzoo, netzoo.github.io) into a single package by\ncreating interfaces between network inference and network\nanalysis methods. Currently, the package has 3 methods for\nnetwork inference including PANDA and its optimized\nimplementation OTTER (network reconstruction using mutliple\nlines of biological evidence), LIONESS (single-sample network\ninference), and EGRET (genotype-specific networks). Network\nanalysis methods include CONDOR (community detection), ALPACA\n(differential community detection), CRANE (significance\nestimation of differential modules), MONSTER (estimation of\nnetwork transition states). 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"page": "downloadGTEx", "title": "Download GTEx files and turn them into ExpressionSet object", "topics": [ "downloadGTEx" ] }, { "page": "dragon", "title": "Run DRAGON in R.", "topics": [ "dragon" ] }, { "page": "el2adj", "title": "Convert bipartite edge list to adjacency mat", "topics": [ "el2adj" ] }, { "page": "el2regulon", "title": "Convert a bipartite edgelist to regulon", "topics": [ "el2regulon" ] }, { "page": "elistAddTags", "title": "Adds \"_A\" to first column and \"_B\" to second column", "topics": [ "elistAddTags" ] }, { "page": "elistIsEdgeOrderEqual", "title": "check if first two columns are identical", "topics": [ "elistIsEdgeOrderEqual" ] }, { "page": "elistRemoveTags", "title": "undo elistAddTags", "topics": [ "elistRemoveTags" ] }, { "page": "elistSort", "title": "Sorts the edge list based on first two columns in alphabetical order", "topics": [ "elistSort" ] }, { "page": "elistToAdjMat", "title": "Converts edge list to adjacency matrix", "topics": [ "elistToAdjMat" ] }, { "page": "exon.size", "title": "Gene length", "topics": [ "exon.size" ] }, { "page": "extractMatrix", "title": "Extract the appropriate matrix", "topics": [ "extractMatrix" ] }, { "page": "filterGenes", "title": "Filter specific genes", "topics": [ "filterGenes" ] }, { "page": "filterLowGenes", "title": "Filter genes that have less than a minimum threshold CPM for a given group/tissue", "topics": [ "filterLowGenes" ] }, { "page": "filterMissingGenes", "title": "Filter genes not expressed in any sample", "topics": [ "filterMissingGenes" ] }, { "page": "filterSamples", "title": "Filter samples", "topics": [ "filterSamples" ] }, { "page": "FindConnectionsForAllHopCounts", "title": "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", "topics": [ "FindConnectionsForAllHopCounts" ] }, { "page": "FindSignificantEdgesForHop", "title": "Find the subnetwork of significant edges n / 2 hops away from each gene.", "topics": [ "FindSignificantEdgesForHop" ] }, { "page": "GenerateNullPANDADistribution", "title": "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.", "topics": [ "GenerateNullPANDADistribution" ] }, { "page": "genes", "title": "Example of a gene list", "topics": [ "genes" ] }, { "page": "isElist", "title": "Check if data frame is an edge list", "topics": [ "isElist" ] }, { "page": "jutterDegree", "title": "CRANE Beta perturbation function. This function will add noice to the node strength sequence.", "topics": [ "jutterDegree" ] }, { "page": "lioness", "title": "Compute LIONESS (Linear Interpolation to Obtain Network Estimates for Single Samples)", "topics": [ "lioness" ] }, { "page": "lionessPy", "title": "Run python implementation of LIONESS", "topics": [ "lionessPy" ] }, { "page": "monster", "title": "MOdeling Network State Transitions from Expression and Regulatory data (MONSTER)", "topics": [ "monster" ] }, { "page": "monsterBereFull", "title": "Bipartite Edge Reconstruction from Expression data (composite method with direct/indirect)", "topics": [ "monsterBereFull" ] }, { "page": "monsterCalculateTmPValues", "title": "Calculate p-values for a tranformation matrix", "topics": [ "monsterCalculateTmPValues" ] }, { "page": "monsterCalculateTmStats", "title": "Calculate statistics for a tranformation matrix", "topics": [ "monsterCalculateTmStats" ] }, { "page": "monsterCheckDataType", "title": "Checks that data is something MONSTER can handle", "topics": [ "monsterCheckDataType" ] }, { "page": "monsterdTFIPlot", "title": "This function plots the Off diagonal mass of an observed Transition Matrix compared to a set of null TMs", "topics": [ "monsterdTFIPlot" ] }, { "page": "monsterGetTm", "title": "monsterGetTm", "topics": [ "monsterGetTm" ] }, { "page": "monsterHclHeatmapPlot", "title": "Transformation matrix plot", "topics": [ "monsterHclHeatmapPlot" ] }, { "page": "monsterMonsterNI", "title": "Bipartite Edge Reconstruction from Expression data", "topics": [ "monsterMonsterNI" ] }, { "page": "monsterPlotMonsterAnalysis", "title": "monsterPlotMonsterAnalysis", "topics": [ "monsterPlotMonsterAnalysis" ] }, { "page": "monsterPrintMonsterAnalysis", "title": "monsterPrintMonsterAnalysis", "topics": [ "monsterPrintMonsterAnalysis" ] }, { "page": "monsterRes", "title": "MONSTER results from example cell-cycle yeast transition", "topics": [ "monsterRes" ] }, { "page": "monsterTransformationMatrix", "title": "Bi-partite network analysis tools", "topics": [ "monsterTransformationMatrix" ] }, { "page": "monsterTransitionNetworkPlot", "title": "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.", "topics": [ "monsterTransitionNetworkPlot" ] }, { "page": "monsterTransitionPCAPlot", "title": "Principal Components plot of transformation matrix", "topics": [ "monsterTransitionPCAPlot" ] }, { "page": "mut.ucec", "title": "Example of mutation data", "topics": [ "mut.ucec" ] }, { "page": "normalizeTissueAware", "title": "Normalize in a tissue aware context", "topics": [ "normalizeTissueAware" ] }, { "page": "otter", "title": "Run OTTER in R", "topics": [ "otter" ] }, { "page": "pandaDiffEdges", "title": "Identify differential edges in two PANDA networks", "topics": [ "pandaDiffEdges" ] }, { "page": "pandaPy", "title": "Run Python implementation PANDA in R", "topics": [ "pandaPy" ] }, { "page": "pandaToAlpaca", "title": "Use two PANDA network to generate an ALPACA result", "topics": [ "pandaToAlpaca" ] }, { "page": "pandaToCondorObject", "title": "Turn PANDA network into a CONDOR object", "topics": [ "pandaToCondorObject" ] }, { "page": "plotCMDS", "title": "Plot classical MDS of dataset", "topics": [ "plotCMDS" ] }, { "page": "plotDensity", "title": "Density plots of columns in a matrix", "topics": [ "plotDensity" ] }, { "page": "plotHeatmap", "title": "Plot heatmap of most variable genes", "topics": [ "plotHeatmap" ] }, { "page": "PlotNetwork", "title": "Plot the networks, using different colors for transcription factors, genes of interest, and additional genes.", "topics": [ "PlotNetwork" ] }, { "page": "priorPp", "title": "Filter low confident edge signs in the prior network using GeneNet", "topics": [ "priorPp" ] }, { "page": "puma", "title": "PANDA using microRNA associations", "topics": [ "puma" ] }, { "page": "qsmooth", "title": "Quantile shrinkage normalization", "topics": [ "qsmooth" ] }, { "page": "qstats", "title": "Compute quantile statistics", "topics": [ "qstats" ] }, { "page": "RunBLOBFISH", "title": "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.", "topics": [ "RunBLOBFISH" ] }, { "page": "runEgret", "title": "Run EGRET in R", "topics": [ "runEgret" ] }, { "page": "sambar", "title": "Main SAMBAR function.", "topics": [ "sambar" ] }, { "page": "sambarConvertgmt", "title": "Convert .gmt files into a binary matrix.", "topics": [ "sambarConvertgmt" ] }, { "page": "sambarCorgenelength", "title": "Normalize gene mutation scores by gene length.", "topics": [ "sambarCorgenelength" ] }, { "page": "sambarDesparsify", "title": "De-sparsify gene-level mutation scores into gene set-level mutation scores.", "topics": [ "sambarDesparsify" ] }, { "page": "SignificantBreadthFirstSearch", "title": "Find all significant edges adjacent to the starting nodes, excluding the nodes specified.", "topics": [ "SignificantBreadthFirstSearch" ] }, { "page": "skin", "title": "Skin RNA-seq data from the GTEx consortium", "topics": [ "skin" ] }, { "page": "small1976", "title": "Pollinator-plant interactions", "topics": [ "small1976" ] }, { "page": "sourcePPI", "title": "Source the Protein-Protein interaction in STRING database", "topics": [ "sourcePPI" ] }, { "page": "spider", "title": "Seeding PANDA Interactions to Derive Epigenetic Regulation", "topics": [ "spider" ] }, { "page": "tiger", "title": "TIGER main function", "topics": [ "tiger" ] }, { "page": "TIGER_expr", "title": "TIGER example expression matrix", "topics": [ "TIGER_expr" ] }, { "page": "TIGER_prior", "title": "TIGER example prior network", "topics": [ "TIGER_prior" ] }, { "page": "visPandaInCytoscape", "title": "Plot PANDA network in Cytoscape", "topics": [ "visPandaInCytoscape" ] }, { "page": "yeast", "title": "Toy data derived from three gene expression datasets and a mapping from transcription factors to genes.", "topics": [ "yeast" ] } ], "_readme": "https://github.com/bioc/netZooR/raw/HEAD/README.md", "_rundeps": [ "abind", "annotate", "AnnotationDbi", "AnnotationForge", "askpass", "assertthat", "backports", "base64", "base64enc", "base64url", "beanplot", "BH", "Biobase", "BiocBaseUtils", "BiocFileCache", "BiocGenerics", "BiocIO", "biocmake", "BiocParallel", "biomaRt", "Biostrings", "bit", "bit64", "bitops", "blob", "bumphunter", "cachem", "Category", "caTools", "checkmate", "chron", "cigarillo", "cli", "clipr", "cluster", "cmdstanr", "codetools", "corpcor", "cpp11", "crayon", "curl", "data.table", "DBI", "dbplyr", "DelayedArray", "DelayedMatrixStats", "digest", "dir.expiry", "distributional", "doParallel", "doRNG", "downloader", "dplyr", "edgeR", "evaluate", "farver", "fastmap", "fdrtool", "filelock", "foreach", "formatR", "fs", "futile.logger", "futile.options", "genefilter", "GeneNet", "generics", "GenomicAlignments", "GenomicFeatures", "GenomicRanges", "GEOquery", "ggdendro", "ggplot2", "glue", 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