To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("PAPi")
In most cases, you don't need to download the package archive at all.
PAPi
Predict metabolic pathway activity based on metabolomics data
Bioconductor version: 3.0
The Pathway Activity Profiling - PAPi - is an R package for predicting the activity of metabolic pathways based solely on a metabolomics data set containing a list of metabolites identified and their respective abundances in different biological samples. PAPi generates hypothesis that improves the final biological interpretation. See Aggio, R.B.M; Ruggiero, K. and Villas-Boas, S.G. (2010) - Pathway Activity Profiling (PAPi): from metabolite profile to metabolic pathway activity. Bioinformatics.
Author: Raphael Aggio
Maintainer: Raphael Aggio <raphael.aggio at gmail.com>
Citation (from within R,
enter citation("PAPi")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("PAPi")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("PAPi")
| R Script | Applying PAPi | |
| PAPi.pdf | ||
| Reference Manual |
Details
| biocViews | MassSpectrometry, Metabolomics, Software |
| Version | 1.6.1 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL(>= 2) |
| Depends | R (>= 2.15.2), svDialogs, KEGGREST |
| Imports | |
| LinkingTo | |
| Suggests | |
| 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 | PAPi_1.6.1.tar.gz |
| Windows Binary | PAPi_1.6.1.zip |
| Mac OS X 10.6 (Snow Leopard) | PAPi_1.6.1.tgz |
| Mac OS X 10.9 (Mavericks) | PAPi_1.6.1.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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