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biocLite("TargetScoreData")
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TargetScoreData
TargetScoreData
Bioconductor version: 3.0
Precompiled and processed miRNA-overexpression fold-changes from 84 Gene Expression Omnibus (GEO) series corresponding to 6 platforms, 77 human cells or tissues, and 113 distinct miRNAs. Accompanied with the data, we also included in this package the sequence feature scores from TargetScanHuman 6.1 including the context+ score and the probabilities of conserved targeting for each miRNA-mRNA interaction. Thus, the user can use these static sequence-based scores together with user-supplied tissue/cell-specific fold-change due to miRNA overexpression to predict miRNA targets using the package TargetScore (download separately)
Author: Yue Li
Maintainer: Yue Li <yueli at cs.toronto.edu>
Citation (from within R,
enter citation("TargetScoreData")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("TargetScoreData")
Documentation
To view documentation for the version of this package installed in your system, start R and enter:
browseVignettes("TargetScoreData")
| R Script | Processed human microRNA-overexpression data from GEO, and sequence information from TargetScan, and targetScore from TargetScore | |
| Reference Manual |
Details
| biocViews | ExperimentData, RNAExpressionData, miRNAoverexpression |
| Version | 1.1.1 |
| License | GPL-2 |
| Depends | |
| Imports | |
| LinkingTo | |
| Suggests | TargetScore, gplots |
| SystemRequirements | |
| Enhances | |
| URL | |
| Depends On Me | |
| Imports Me | |
| Suggests Me | TargetScore |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | TargetScoreData_1.1.1.tar.gz |
| Windows Binary | TargetScoreData_1.1.1.zip |
| Mac OS X 10.6 (Snow Leopard) | TargetScoreData_1.1.1.tgz |
| Mac OS X 10.9 (Mavericks) | TargetScoreData_1.1.1.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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