dnet 1.1.2

Documentations

Network analysis and visualisation functions

These functions are used for network analysis and visualisation, including: identification of gene-active networks, and network-based sample classifications and visualisations on 2D sample landscape.

  • dNetPipeline
    Function to setup the pipeline for finding maximum-scoring subgraph from an input graph and the signficance imposed on its nodes
  • dNetReorder
    Function to reorder the multiple graph colorings within a sheet-shape rectangle grid
  • dNetConfidence
    Function to append the confidence information from the source graphs into the target graph
  • dPvalAggregate
    Function to aggregate p values
  • dNetInduce
    Function to generate a subgraph induced by given vertices and their k nearest neighbors
  • dBUMfit
    Function to fit a p-value distribution under beta-uniform mixture model
  • dBUMscore
    Function to transform p-values into scores according to the fitted beta-uniform mixture model and/or after controlling false discovery rate
  • 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
  • dNetFind
    Function to find heuristically maximum scoring subgraph
  • dCommSignif
    Function to test the significance of communities within a graph
  • dContrast
    Function to help build the contrast matrix
  • visNet
    Function to visualise a graph object of class "igraph" or "graphNEL"
  • visNetMul
    Function to visualise the same graph but with multiple graph node colorings according to input data matrix
  • visNetAnimate
    Function to animate the same graph but with multiple graph node colorings according to input data matrix
  • visNetReorder
    Function to visualise the multiple graph colorings reorded within a sheet-shape rectangle grid
  • visNetArc
    Function to visualise an igraph object via arc diagram
  • visNetCircle
    Function to visualise an igraph object via circle diagram
  • visBoxplotAdv
    Function to visualise a data frame using advanced boxplot
  • dRDataLoader
    Function to load dnet built-in RData
  • dFunArgs
    Function to assign (and evaluate) arguments with default values for an input function

Random Walk with Restart (RWR)

These functions are used for RWR-based analysis, including: calculation of network affinity and estimation of contact strength between samples.

  • dRWR
    Function to implement Random Walk with Restart (RWR) on the input graph
  • 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
  • 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
  • dCheckParallel
    Function to check whether parallel computing should be used and how

Enrichment analysis and visualisation functions

These functions are used for enrichment analysis and visualisation, including: enrichment analysis (Fisher's exact test, Hypergeometric test or Binomial test) that can account for the hierarchy of the ontology; and gene set enrichment analysis (GSEA).

  • dEnricher
    Function to conduct enrichment analysis given the input data and the ontology in query
  • dEnricherView
    Function to view enrichment results of dEnricher
  • dGSEA
    Function to conduct gene set enrichment analysis given the input data and the ontology in query
  • dGSEAview
    Function to view enrichment results in a sample-specific manner
  • dGSEAwrite
    Function to write out enrichment results
  • visGSEA
    Function to visualise running enrichment score for a given sample and a gene set

Built-in ontologies, and supporting functions for analysis and visualisation

These ontologies each are represented as a direct acyclic graph (DAG). DAG is stored as an object of the class 'igraph', which can be processed by various analysis and visualisation functions.

  • ig.GOMF
    Gene Ontology Molecular Function (GOMF).
  • ig.GOBP
    Gene Ontology Biological Process (GOBP).
  • ig.GOCC
    Gene Ontology Cellular Component (GOCC).
  • ig.HPPA
    Human Phenotype Phenotypic Abnormality (HPPA).
  • ig.HPMI
    Human Phenotype Mode of Inheritance (HPMI).
  • ig.HPCM
    Human Phenotype Clinical Modifier (HPCM).
  • ig.HPMA
    Human Phenotype Mortality Aging (HPMA).
  • ig.DO
    Disease Ontology (DO).
  • ig.MP
    Mammalian Phenotype (MP).
  • dDAGinduce
    Function to generate a subgraph of a direct acyclic graph (DAG) induced by given vertices
  • dDAGreverse
    Function to reverse the edge direction of a direct acyclic graph (DAG)
  • dDAGroot
    Function to find the root node of a direct acyclic graph (DAG)
  • dDAGtip
    Function to find the tip node(s) of a direct acyclic graph (DAG)
  • dDAGlevel
    Function to define/calculate the level of nodes in a direct acyclic graph (DAG)
  • dDAGannotate
    Function to generate a subgraph of a direct acyclic graph (DAG) induced by the input annotation data
  • dDAGancestor
    Function to find common ancestors of two terms/nodes from a direct acyclic graph (DAG)
  • dDAGtermSim
    Function to calculate pair-wise semantic similarity between input terms based on a direct acyclic graph (DAG) with annotated data
  • dDAGgeneSim
    Function to calculate pair-wise semantic similarity between genes based on a direct acyclic graph (DAG) with annotated data
  • visDAG
    Function to visualise a direct acyclic graph (DAG) with node colorings according to a named input data vector (if provided)

Built-in databases in human

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in human (Hs; tax_id=9606).

  • org.Hs.eg
    Human Entrez Genes (EG).
  • org.Hs.egDO
    Annotations of Human Entrez Genes (EG) by Disease Ontology (DO).
  • org.Hs.egGOBP
    Annotations of Human Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Hs.egGOCC
    Annotations of Human Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Hs.egGOMF
    Annotations of Human Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Hs.egHPMI
    Annotations of Human Entrez Genes (EG) by Human Phenotype Mode of Inheritance (HPMI).
  • org.Hs.egHPCM
    Annotations of Human Entrez Genes (EG) by Human Phenotype Clinical Modifier (HPCM).
  • org.Hs.egHPMA
    Annotations of Human Entrez Genes (EG) by Human Phenotype Mortality Aging (HPMA).
  • org.Hs.egHPPA
    Annotations of Human Entrez Genes (EG) by Human Phenotype Phenotypic Abnormality (HPPA).
  • org.Hs.egMP
    Annotations of Human Entrez Genes (EG) by Mammalian Phenotype (MP).
  • org.Hs.egPS
    Annotations of Human Entrez Genes (EG) by phylostratific age (PS).
  • org.Hs.egSF
    Annotations of Human Entrez Genes (EG) by domain superfamilies (SF).
  • org.Hs.egDGIdb
    Annotations of Human Entrez Genes (EG) by DGIdb categories.
  • org.Hs.string
    Human functional protein association network from STRING (version 10).

Built-in genesets in human

These genesets are derived from the molecular signatures database (Msigdb) in human (Hs).

Built-in databases in mouse

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in mouse (Mm; tax_id=10090).

  • org.Mm.eg
    Mouse Entrez Genes (EG).
  • org.Mm.egDO
    Annotations of Mouse Entrez Genes (EG) by Disease Ontology (DO).
  • org.Mm.egGOBP
    Annotations of Mouse Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Mm.egGOCC
    Annotations of Mouse Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Mm.egGOMF
    Annotations of Mouse Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Mm.egHPMI
    Annotations of Mouse Entrez Genes (EG) by Human Phenotype Mode of Inheritance (HPMI).
  • org.Mm.egHPCM
    Annotations of Mouse Entrez Genes (EG) by Human Phenotype Clinical Modifier (HPCM).
  • org.Mm.egHPMA
    Annotations of Mouse Entrez Genes (EG) by Human Phenotype Mortality Aging (HPMA).
  • org.Mm.egHPPA
    Annotations of Mouse Entrez Genes (EG) by Human Phenotype Phenotypic Abnormality (HPPA).
  • org.Mm.egMP
    Annotations of Mouse Entrez Genes (EG) by Mammalian Phenotype (MP).
  • org.Mm.egPS
    Annotations of Mouse Entrez Genes (EG) by phylostratific age (PS).
  • org.Mm.egSF
    Annotations of Mouse Entrez Genes (EG) by domain superfamilies (SF).
  • org.Mm.string
    Mouse functional protein association network from STRING (version 10).

Built-in databases in arabidopsis

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in arabidopsis (At; tax_id=3702).

  • org.At.eg
    Arabidopsis Entrez Genes (EG).
  • org.At.egGOBP
    Annotations of Arabidopsis Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.At.egGOCC
    Annotations of Arabidopsis Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.At.egGOMF
    Annotations of Arabidopsis Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.At.egPS
    Annotations of Arabidopsis Entrez Genes (EG) by phylostratific age (PS).
  • org.At.egSF
    Annotations of Arabidopsis Entrez Genes (EG) by domain superfamilies (SF).
  • org.At.string
    Arabidopsis functional protein association network from STRING (version 10).

Built-in databases in c.elegans

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in c.elegans (Ce; tax_id=6239).

  • org.Ce.eg
    C.elegans Entrez Genes (EG).
  • org.Ce.egGOBP
    Annotations of C.elegans Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Ce.egGOCC
    Annotations of C.elegans Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Ce.egGOMF
    Annotations of C.elegans Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Ce.egPS
    Annotations of C.elegans Entrez Genes (EG) by phylostratific age (PS).
  • org.Ce.egSF
    Annotations of C.elegans Entrez Genes (EG) by domain superfamilies (SF).
  • org.Ce.string
    C.elegans functional protein association network from STRING (version 10).

Built-in databases in fruitfly

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in fruitfly (Dm; tax_id=7227).

  • org.Dm.eg
    Fruitfly Entrez Genes (EG).
  • org.Dm.egGOBP
    Annotations of Fruitfly Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Dm.egGOCC
    Annotations of Fruitfly Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Dm.egGOMF
    Annotations of Fruitfly Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Dm.egPS
    Annotations of Fruitfly Entrez Genes (EG) by phylostratific age (PS).
  • org.Dm.egSF
    Annotations of Fruitfly Entrez Genes (EG) by domain superfamilies (SF).
  • org.Dm.string
    Fruitfly functional protein association network from STRING (version 10).

Built-in databases in zebrafish

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in zebrafish (Da; tax_id=7955).

  • org.Da.eg
    Zebrafish Entrez Genes (EG).
  • org.Da.egGOBP
    Annotations of Zebrafish Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Da.egGOCC
    Annotations of Zebrafish Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Da.egGOMF
    Annotations of Zebrafish Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Da.egPS
    Annotations of Zebrafish Entrez Genes (EG) by phylostratific age (PS).
  • org.Da.egSF
    Annotations of Zebrafish Entrez Genes (EG) by domain superfamilies (SF).
  • org.Da.string
    Zebrafish functional protein association network from STRING (version 10).

Built-in databases in rat

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in rat (Rn; tax_id=10116).

  • org.Rn.eg
    Rat Entrez Genes (EG).
  • org.Rn.egGOBP
    Annotations of Rat Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Rn.egGOCC
    Annotations of Rat Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Rn.egGOMF
    Annotations of Rat Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Rn.egPS
    Annotations of Rat Entrez Genes (EG) by phylostratific age (PS).
  • org.Rn.egSF
    Annotations of Rat Entrez Genes (EG) by domain superfamilies (SF).
  • org.Rn.string
    Rat functional protein association network from STRING (version 10).

Built-in databases in chicken

These databases are used for analysis (i.e. enrichment, evolution and network analysis) in chicken (Gg; tax_id=9031).

  • org.Gg.eg
    Chicken Entrez Genes (EG).
  • org.Gg.egGOBP
    Annotations of Chicken Entrez Genes (EG) by Gene Ontology Biological Process (GOBP).
  • org.Gg.egGOCC
    Annotations of Chicken Entrez Genes (EG) by Gene Ontology Cellular Component (GOCC).
  • org.Gg.egGOMF
    Annotations of Chicken Entrez Genes (EG) by Gene Ontology Molecular Function (GOMF).
  • org.Gg.egPS
    Annotations of Chicken Entrez Genes (EG) by phylostratific age (PS).
  • org.Gg.egSF
    Annotations of Chicken Entrez Genes (EG) by domain superfamilies (SF).
  • org.Gg.string
    Chicken functional protein association network from STRING (version 10).

Built-in datasets

These datasets are used for the demos.

  • Hiratani_TableS1(RT, CpG, EX)
    Mouse multilayer omics dataset from Hiratani et al. (2010)
  • CLL
    Transcriptome dataset for Chronic Lymphocytic Leukemia patients from Chuang et al. (2012)
  • TCGA_mutations
    TCGA mutational profiles across 12 major cancer types from Kandoth et al. (2013)