To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ACME")
In most cases, you don't need to download the package archive at all.
ACME
Algorithms for Calculating Microarray Enrichment (ACME)
Bioconductor version: 2.14
ACME (Algorithms for Calculating Microarray Enrichment) is a set of tools for analysing tiling array ChIP/chip, DNAse hypersensitivity, or other experiments that result in regions of the genome showing "enrichment". It does not rely on a specific array technology (although the array should be a "tiling" array), is very general (can be applied in experiments resulting in regions of enrichment), and is very insensitive to array noise or normalization methods. It is also very fast and can be applied on whole-genome tiling array experiments quite easily with enough memory.
Author: Sean Davis <sdavis2 at mail.nih.gov>
Maintainer: Sean Davis <sdavis2 at mail.nih.gov>
Citation (from within R,
enter citation("ACME")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("ACME")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("ACME")
| R Script | ACME | |
| Reference Manual |
Details
| biocViews | Microarray, Normalization, Software, Technology |
| Version | 2.20.0 |
| In Bioconductor since | BioC 2.0 (R-2.5) |
| License | GPL (>= 2) |
| Depends | R (>= 2.10), Biobase(>= 2.5.5), methods |
| Imports | graphics, stats |
| Suggests | |
| System Requirements | |
| URL | http://watson.nci.nih.gov/~sdavis |
| Depends On Me | |
| Imports Me | |
| Suggests Me |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | ACME_2.20.0.tar.gz |
| Windows Binary | ACME_2.20.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | ACME_2.20.0.tgz |
| Mac OS X 10.9 (Mavericks) | ACME_2.20.0.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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