To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("MLSeq")
In most cases, you don't need to download the package archive at all.
MLSeq
Machine learning interface for RNA-Seq data
Bioconductor version: 3.0
This package applies several machine learning methods, including SVM, bagSVM, Random Forest and CART, to RNA-Seq data.
Author: Gokmen Zararsiz, Dincer Goksuluk, Selcuk Korkmaz, Vahap Eldem, Izzet Parug Duru, Turgay Unver, Ahmet Ozturk
Maintainer: Gokmen Zararsiz <gokmenzararsiz at erciyes.edu.tr>
Citation (from within R,
enter citation("MLSeq")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("MLSeq")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("MLSeq")
| R Script | MLSeq | |
| Reference Manual | ||
| Text | README |
Details
| biocViews | Classification, Clustering, Software |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.14 (R-3.1) |
| License | GPL(>=2) |
| Depends | R (>= 3.0.0), caret, DESeq2, Biobase, limma, randomForest, edgeR |
| Imports | methods |
| LinkingTo | |
| Suggests | knitr, e1071, kernlab, earth, ellipse, fastICA, gam, ipred, klaR, MASS, mda, mgcv, mlbench, nnet, party, pls, pROC, proxy, RANN, spls, affy |
| 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 | MLSeq_1.2.0.tar.gz |
| Windows Binary | MLSeq_1.2.0.zip |
| Mac OS X 10.6 (Snow Leopard) | MLSeq_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | MLSeq_1.2.0.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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