To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("HCsnip")
In most cases, you don't need to download the package archive at all.
HCsnip
Semi-supervised adaptive-height snipping of the Hierarchical Clustering tree
Bioconductor version: 3.0
Decompose given hierarchical clustering tree into non-overlapping clusters in a semi-supervised way by using available patients follow-up information as guidance. Contains functions for snipping HC tree, various cluster quality evaluation criteria, assigning new patients to one of the two given HC trees, testing the significance of clusters with permutation argument and clusters visualization using sample's molecular entropy.
Author: Askar Obulkasim
Maintainer: Askar Obulkasim <askar703 at gmail.com>
Citation (from within R,
enter citation("HCsnip")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("HCsnip")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("HCsnip")
| R Script | HCsnip | |
| Reference Manual |
Details
| biocViews | Clustering, GeneExpression, Microarray, Software, aCGH |
| Version | 1.6.0 |
| In Bioconductor since | BioC 2.12 (R-3.0) |
| License | GPL (>= 2) |
| Depends | R (>= 2.10.0), survival, coin, fpc, clusterRepro, impute, randomForestSRC, sm, sigaR, Biobase |
| 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 | HCsnip_1.6.0.tar.gz |
| Windows Binary | HCsnip_1.6.0.zip |
| Mac OS X 10.6 (Snow Leopard) | HCsnip_1.6.0.tgz |
| Mac OS X 10.9 (Mavericks) | HCsnip_1.6.0.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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