To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("mmnet")
In most cases, you don't need to download the package archive at all.
mmnet
A metagenomic pipeline for systems biology
Bioconductor version: 3.0
This package gives the implementations microbiome metabolic network constructing and analyzing. It introduces a unique metagenomic systems biology approach, mapping metagenomic data to the KEGG global metabolic pathway and constructing a systems-level network. The system-level network and the next topological analysis will be of great help to analysis the various functional properties, including regulation and metabolic functionality of the metagenome.
Author: Yang Cao, Fei Li
Maintainer: Yang Cao <yiluheihei at gmail.com>, Fei Li <pittacus at gmail.com>
Citation (from within R,
enter citation("mmnet")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("mmnet")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("mmnet")
| R Script | mmnet | |
| Reference Manual |
Details
| biocViews | GraphsAndNetwork, Microbiome, Pathways, Sequencing, Software, SystemsBiology |
| Version | 1.4.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | GPL (>= 2) |
| Depends | R (>= 2.14), igraph, biom |
| Imports | Biobase, RJSONIO, stringr, reshape2, ggplot2, KEGGREST, plyr, XML, RCurl, flexmix, Matrix, methods, tools |
| LinkingTo | |
| Suggests | RCytoscape, graph, knitr |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | mmnet_1.4.0.tar.gz |
| Windows Binary | mmnet_1.4.0.zip |
| Mac OS X 10.6 (Snow Leopard) | mmnet_1.4.0.tgz |
| Mac OS X 10.9 (Mavericks) | mmnet_1.4.0.tgz |
| Browse/checkout source | (username/password: readonly) |
| Package Downloads Report | Download Stats |
Common Bioconductor workflows include:
- Oligonucleotide Arrays
- High-throughput Sequencing
- Counting Reads for Differential Expression (parathyroideSE vignette)
- Annotation
- Annotating Variants
- Annotating Ranges
- Flow Cytometry and other assays
- Candidate Binding Sites for Known Transcription Factors
- Cloud-enabled cis-eQTL search and annotation
- RNA-Seq workflow: gene-level exploratory analysis and differential expression
- Changing genomic coordinate systems with rtracklayer::liftOver
- Mass spectrometry and proteomics data analysis
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