BioNet
Routines for the functional analysis of biological networks
Bioconductor version: 2.10
This package provides functions for the integrated analysis of protein-protein interaction networks and the detection of functional modules. Different datasets can be integrated into the network by assigning p-values of statistical tests to the nodes of the network. E.g. p-values obtained from the differential expression of the genes from an Affymetrix array are assigned to the nodes of the network. By fitting a beta-uniform mixture model and calculating scores from the p-values, overall scores of network regions can be calculated and an integer linear programming algorithm identifies the maximum scoring subnetwork.
Author: Marcus Dittrich and Daniela Beisser
Maintainer: Marcus Dittrich <marcus.dittrich at biozentrum.uni-wuerzburg.de>
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("BioNet")
To cite this package in a publication, start R and enter:
citation("BioNet")
Documentation
| R Script | BioNet Tutorial | |
| R Script | bum1.pdf | |
| R Script | bum2.pdf | |
| R Script | cytoscape.pdf | |
| R Script | prec_recall_large.pdf | |
| R Script | prec_recall_small.pdf | |
| R Script | Tutorial-3dplot.pdf | |
| Reference Manual |
Details
| biocViews | Bioinformatics, DataImport, DifferentialExpression, GeneExpression, GraphsAndNetworks, Microarray, Software, Visualization |
| Depends | R (>= 2.10.0), Biobase, graph, RBGL |
| Imports | igraph0, AnnotationDbi |
| Suggests | rgl, impute, DLBCL, genefilter, xtable, ALL, limma, hgu95av2.db, XML |
| System Requirements | |
| License | GPL (>= 2) |
| URL | http://bionet.bioapps.biozentrum.uni-wuerzburg.de/ |
| Depends On Me | HTSanalyzeR |
| Imports Me | HTSanalyzeR |
| Suggests Me | |
| Version | 1.14.3 |
| Since | Bioconductor 2.7 (R-2.12) |
Package Downloads
| Package Source | BioNet_1.14.3.tar.gz |
| Windows Binary | BioNet_1.14.3.zip (32- & 64-bit) |
| MacOS 10.5 (Leopard) binary | BioNet_1.14.3.tgz |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Annotation
- Variants
- Flow Cytometry and other assays
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