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dnet (version 1.1.7)

Integrative Analysis of Omics Data in Terms of Network, Evolution and Ontology

Description

The focus of the dnet by Fang and Gough (2014) is to make sense of omics data (such as gene expression and mutations) from different angles including: integration with molecular networks, enrichments using ontologies, and relevance to gene evolutionary ages. Integration is achieved to identify a gene subnetwork from the whole gene network whose nodes/genes are labelled with informative data (such as the significant levels of differential expression or survival risks). To help make sense of identified gene networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific gene age information in major organisms including: human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. Add-on functionalities are supports for calculating semantic similarity between ontology terms (and between genes) and for calculating network affinity based on random walk; both can be done via high-performance parallel computing.

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Install

install.packages('dnet')

Monthly Downloads

242

Version

1.1.7

License

GPL-2

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Last Published

February 20th, 2020

Functions in dnet (1.1.7)

visBoxplotAdv

Function to visualise a data frame using advanced boxplot
visNetAnimate

Function to animate the same graph but with multiple graph node colorings according to input data matrix
dDAGreverse

Function to reverse the edge direction of a direct acyclic graph (DAG)
dDAGinduce

Function to generate a subgraph of a direct acyclic graph (DAG) induced by given vertices
dDAGgeneSim

Function to calculate pair-wise semantic similarity between genes based on a direct acyclic graph (DAG) with annotated data
dCommSignif

Function to test the significance of communities within a graph
dGSEAview

Function to view enrichment results in a sample-specific manner
dCheckParallel

Function to check whether parallel computing should be used and how
visNet

Function to visualise a graph object of class "igraph" or "graphNEL"
dSVDsignif

Function to obtain SVD-based gene significance from the input gene-sample matrix
dFDRscore

Function to transform fdr into scores according to log-likelihood ratio between the true positives and the false positivies and/or after controlling false discovery rate
dRWR

Function to implement Random Walk with Restart (RWR) on the input graph
visGSEA

Function to visualise running enrichment score for a given sample and a gene set
dGSEAwrite

Function to write out enrichment results
dDAGroot

Function to find the root node of a direct acyclic graph (DAG)
dDAGlevel

Function to define/calculate the level of nodes in a direct acyclic graph (DAG)
dNetFind

Function to find heuristically maximum scoring subgraph
dRWRcontact

Function to estimate RWR-based contact strength between samples from an input gene-sample data matrix, an input graph and its pre-computed affinity matrix
visDAG

Function to visualise a direct acyclic graph (DAG) with node colorings according to a named input data vector (if provided)
dFunArgs

Function to assign (and evaluate) arguments with default values for an input function
dDAGannotate

Function to generate a subgraph of a direct acyclic graph (DAG) induced by the input annotation data
dDAGtermSim

Function to calculate pair-wise semantic similarity between input terms based on a direct acyclic graph (DAG) with annotated data
dNetConfidence

Function to append the confidence information from the source graphs into the target graph
dPvalAggregate

Function to aggregate p values
dContrast

Function to help build the contrast matrix
dDAGancestor

Function to find common ancestors of two terms/nodes from a direct acyclic graph (DAG)
dDAGtip

Function to find the tip node(s) of a direct acyclic graph (DAG)
dGSEA

Function to conduct gene set enrichment analysis given the input data and the ontology in query
dRDataLoader

Function to load dnet built-in RData
dRWRpipeline

Function to setup a pipeine to estimate RWR-based contact strength between samples from an input gene-sample data matrix and an input graph
visNetMul

Function to visualise the same graph but with multiple graph node colorings according to input data matrix
visNetArc

Function to visualise an igraph object via arc diagram
visNetReorder

Function to visualise the multiple graph colorings reorded within a sheet-shape rectangle grid
dNetInduce

Function to generate a subgraph induced by given vertices and their k nearest neighbors
dNetReorder

Function to reorder the multiple graph colorings within a sheet-shape rectangle grid
visNetCircle

Function to visualise an igraph object via circle diagram
dEnricher

Function to conduct enrichment analysis given the input data and the ontology in query
dBUMscore

Function to transform p-values into scores according to the fitted beta-uniform mixture model and/or after controlling false discovery rate
dEnricherView

Function to view enrichment results of dEnricher
dBUMfit

Function to fit a p-value distribution under beta-uniform mixture model
dNetPipeline

Function to setup the pipeline for finding maximum-scoring subgraph from an input graph and the signficance imposed on its nodes
ig.HPPA

Human Phenotype Phenotypic Abnormality (HPPA).
org.Hs.egHPPA

Annotations of Human Entrez Genes (EG) by Human Phenotype Phenotypic Abnormality (HPPA).