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source("http://bioconductor.org/biocLite.R")
biocLite("cancerclass")
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cancerclass
Development and validation of diagnostic tests from high-dimensional molecular data
Bioconductor version: 2.14
The classification protocol starts with a feature selection step and continues with nearest-centroid classification. The accurarcy of the predictor can be evaluated using training and test set validation, leave-one-out cross-validation or in a multiple random validation protocol. Methods for calculation and visualization of continuous prediction scores allow to balance sensitivity and specificity and define a cutoff value according to clinical requirements.
Author: Jan Budczies, Daniel Kosztyla
Maintainer: Daniel Kosztyla <danielkossi at hotmail.com>
Citation (from within R,
enter citation("cancerclass")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("cancerclass")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("cancerclass")
| R Script | Cancerclass: An R package for development and validation of diagnostic tests from high-dimensional molecular data | |
| Reference Manual |
Details
| biocViews | Cancer, Classification, Microarray, Software, Visualization |
| Version | 1.8.0 |
| In Bioconductor since | BioC 2.11 (R-2.15) |
| License | GPL 3 |
| Depends | R (>= 2.14.0), Biobase, binom, methods, stats |
| Imports | |
| Suggests | cancerdata |
| System Requirements | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | cancerclass_1.8.0.tar.gz |
| Windows Binary | cancerclass_1.8.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | cancerclass_1.8.0.tgz |
| Mac OS X 10.9 (Mavericks) | cancerclass_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
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