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dnet (version 1.0.0)

Dynamic NETworks via integrative analysis of digitised data in terms of network, ontology and evolution

Description

The 'dnet' package is initiated to fill in the need of an open-source tool for digitised data in an integrative manner. More specifically, dnet intends to analyse the biological network whose nodes/genes are associated with digitised information such as expression levels across samples. To help make sense of identified networks, enrichment analysis is also supported using a wide variety of pre-compiled ontologies and phylostratific age information in major organisms including human, mouse, rat, chicken, C.elegans, fruit fly, zebrafish and arabidopsis. In summary, dnet aims to deliver an eye-intuitive tool with rich visuals but less inputs.

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Version

Install

install.packages('dnet')

Monthly Downloads

46

Version

1.0.0

License

GPL-2

Maintainer

Hai Fang

Last Published

April 9th, 2014

Functions in dnet (1.0.0)

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

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

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

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

Function to view enrichment results in a sample-specific manner

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

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

Function to write out enrichment results

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

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

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

Function to find heuristically maximum scoring module

Function to test the significance of communities within a graph

Function to visualise input data matrix using heatmap

Function to visualise input data matrix using advanced heatmap

Function to visualise an igraph object via circle diagram

Function to aggregate p values

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

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

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

Function to visualise an igraph object via arc diagram

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

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

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

Function to define a colormap

visTreeBootstrap

Function to build and visualise the bootstrapped tree

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

Function to help build the contrast matrix

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

Function to add transparent (alpha) into colors

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

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

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

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

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