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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("imageHTS")
In most cases, you don't need to download the package archive at all.
imageHTS
Analysis of high-throughput microscopy-based screens
Bioconductor version: 3.0
imageHTS is an R package dedicated to the analysis of high-throughput microscopy-based screens. The package provides a modular and extensible framework to segment cells, extract quantitative cell features, predict cell types and browse screen data through web interfaces. Designed to operate in distributed environments, imageHTS provides a standardized access to remote data and facilitates the dissemination of high-throughput microscopy-based datasets.
Author: Gregoire Pau, Xian Zhang, Michael Boutros, Wolfgang Huber
Maintainer: Joseph Barry <joseph.barry at embl.de>
Citation (from within R,
enter citation("imageHTS")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("imageHTS")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("imageHTS")
| R Script | Analysis of high-throughput microscopy-based screens with imageHTS | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | CellBasedAssays, Preprocessing, Software, Visualization |
| Version | 1.16.0 |
| In Bioconductor since | BioC 2.7 (R-2.12) |
| License | LGPL-2.1 |
| Depends | R (>= 2.9.0), EBImage(>= 4.3.12), cellHTS2(>= 2.10.0) |
| Imports | tools, Biobase, hwriter, methods, vsn, stats, utils, e1071 |
| LinkingTo | |
| Suggests | BiocStyle, MASS |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | phenoDist |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | imageHTS_1.16.0.tar.gz |
| Windows Binary | imageHTS_1.16.0.zip |
| Mac OS X 10.6 (Snow Leopard) | imageHTS_1.16.0.tgz |
| Mac OS X 10.9 (Mavericks) | imageHTS_1.16.0.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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