To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("lmdme")
In most cases, you don't need to download the package archive at all.
lmdme
Linear Model decomposition for Designed Multivariate Experiments
Bioconductor version: 3.0
linear ANOVA decomposition of Multivariate Designed Experiments implementation based on limma lmFit. Features: i)Flexible formula type interface, ii) Fast limma based implementation, iii) p-values for each estimated coefficient levels in each factor, iv) F values for factor effects and v) plotting functions for PCA and PLS.
Author: Cristobal Fresno and Elmer A. Fernandez
Maintainer: Cristobal Fresno <cfresno at bdmg.com.ar>
Citation (from within R,
enter citation("lmdme")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("lmdme")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("lmdme")
| R Script | lmdme: linear model framework for PCA/PLS analysis of ANOVA decomposition on Designed Multivariate Experiments in R | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Cancer, DifferentialExpression, ExperimentData, Microarray, OneChannel, Software, TwoChannel, Visualization |
| Version | 1.8.0 |
| In Bioconductor since | BioC 2.11 (R-2.15) |
| License | GPL (>=2) |
| Depends | R (>= 2.14.1), pls, stemHypoxia |
| Imports | stats, methods, limma |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | parallel |
| URL | http://www.bdmg.com.ar/?page_id=38 |
| Depends On Me | |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | lmdme_1.8.0.tar.gz |
| Windows Binary | lmdme_1.8.0.zip |
| Mac OS X 10.6 (Snow Leopard) | lmdme_1.8.0.tgz |
| Mac OS X 10.9 (Mavericks) | lmdme_1.8.0.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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