To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("prada")
In most cases, you don't need to download the package archive at all.
prada
Data analysis for cell-based functional assays
Bioconductor version: 2.14
Tools for analysing and navigating data from high-throughput phenotyping experiments based on cellular assays and fluorescent detection (flow cytometry (FACS), high-content screening microscopy).
Author: Florian Hahne <florian.hahne at novartis.com>, Wolfgang Huber <huber at ebi.ac.uk>, Markus Ruschhaupt, Joern Toedling <toedling at ebi.ac.uk>, Joseph Barry <joseph.barry at embl.de>
Maintainer: Florian Hahne <florian.hahne at novartis.com>
Citation (from within R,
enter citation("prada")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("prada")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("prada")
| R Script | Combining prada output and cellHTS | |
| R Script | Removal of contaminants from FACS data | |
| Reference Manual |
Details
| biocViews | CellBasedAssays, Software, Visualization |
| Version | 1.40.0 |
| In Bioconductor since | BioC 1.6 (R-2.1) or earlier |
| License | LGPL |
| Depends | R (>= 2.10), Biobase, RColorBrewer, grid, methods, rrcov |
| Imports | Biobase, BiocGenerics, graphics, grDevices, grid, MASS, methods, RColorBrewer, rrcov, stats4, utils |
| Suggests | cellHTS, tcltk |
| System Requirements | |
| URL | |
| Depends On Me | cellHTS, domainsignatures, facsDorit, RNAither |
| Imports Me | cellHTS2 |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | prada_1.40.0.tar.gz |
| Windows Binary | prada_1.40.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | prada_1.40.0.tgz |
| Mac OS X 10.9 (Mavericks) | prada_1.40.0.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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