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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("flowClust")
In most cases, you don't need to download the package archive at all.
flowClust
Clustering for Flow Cytometry
Bioconductor version: 2.14
Robust model-based clustering using a t-mixture model with Box-Cox transformation. Note: users should have GSL installed. Windows users: 'consult the README file available in the inst directory of the source distribution for necessary configuration instructions'.
Author: Raphael Gottardo <raph at stat.ubc.ca>, Kenneth Lo <c.lo at stat.ubc.ca>, Greg Finak <gfinak at fhcrc.org>
Maintainer: Greg Finak <gfinak at fhcrc.org>, Mike Jiang <wjiang2 at fhcrc.org>
Citation (from within R,
enter citation("flowClust")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("flowClust")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("flowClust")
| R Script | flowClust package | |
| Reference Manual | ||
| Text | README | |
| Text | NEWS |
Details
| biocViews | Clustering, FlowCytometry, Software, Visualization |
| Version | 3.4.10 |
| In Bioconductor since | BioC 2.2 (R-2.7) |
| License | Artistic-2.0 |
| Depends | R (>= 2.5.0), methods, Biobase, graph, RBGL, ellipse, flowViz, mnormt, corpcor, flowCore, clue |
| Imports | BiocGenerics, MCMCpack |
| Suggests | parallel |
| System Requirements | |
| URL | |
| Depends On Me | flowMerge, flowTrans |
| Imports Me | flowPhyto, flowTrans, flowType, openCyto |
| Suggests Me | BiocGenerics |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | flowClust_3.4.10.tar.gz |
| Windows Binary | flowClust_3.4.10.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | flowClust_3.4.10.tgz |
| Mac OS X 10.9 (Mavericks) | flowClust_3.4.10.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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