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source("http://bioconductor.org/biocLite.R")
biocLite("TargetScore")
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TargetScore
TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information
Bioconductor version: 2.14
Infer the posterior distributions of microRNA targets by probabilistically modelling the likelihood microRNA-overexpression fold-changes and sequence-based scores. Variaitonal Bayesian Gaussian mixture model (VB-GMM) is applied to log fold-changes and sequence scores to obtain the posteriors of latent variable being the miRNA targets. The final targetScore is computed as the sigmoid-transformed fold-change weighted by the averaged posteriors of target components over all of the features.
Author: Yue Li
Maintainer: Yue Li <yueli at cs.toronto.edu>
Citation (from within R,
enter citation("TargetScore")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("TargetScore")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("TargetScore")
| R Script | TargetScore: Infer microRNA targets using microRNA-overexpression data and sequence information | |
| Reference Manual | ||
| Text | NEWS |
Details
| biocViews | Software, miRNA |
| Version | 1.2.0 |
| In Bioconductor since | BioC 2.13 (R-3.0) |
| License | GPL-2 |
| Depends | pracma, Matrix |
| Imports | |
| Suggests | TargetScoreData, gplots, Biobase, GEOquery |
| System Requirements | |
| URL | http://www.cs.utoronto.ca/~yueli/software.html |
| Depends On Me | |
| Imports Me | |
| Suggests Me | TargetScoreData |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | TargetScore_1.2.0.tar.gz |
| Windows Binary | TargetScore_1.2.0.zip (32- & 64-bit) |
| Mac OS X 10.6 (Snow Leopard) | TargetScore_1.2.0.tgz |
| Mac OS X 10.9 (Mavericks) | TargetScore_1.2.0.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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