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To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("LungCancerACvsSCCGEO")
In most cases, you don't need to download the package archive at all.
LungCancerACvsSCCGEO
A lung cancer dataset that can be used with maPredictDSC package for developing outcome prediction models from Affymetrix CEL files.
Bioconductor version: 3.0
This package contains 30 Affymetrix CEL files for 7 Adenocarcinoma (AC) and 8 Squamous cell carcinoma (SCC) lung cancer samples taken at random from 3 GEO datasets (GSE10245, GSE18842 and GSE2109) and other 15 samples from a dataset produced by the organizers of the IMPROVER Diagnostic Signature Challenge available from GEO (GSE43580).
Author: Adi Laurentiu Tarca <atarca at med.wayne.edu>
Maintainer: Adi Laurentiu Tarca <atarca at med.wayne.edu>
Citation (from within R,
enter citation("LungCancerACvsSCCGEO")):
Installation
To install this package, start R and enter:
source("http://bioconductor.org/biocLite.R")
biocLite("LungCancerACvsSCCGEO")
Documentation
| Reference Manual |
Details
| biocViews | Classification, ExperimentData, Microarray |
| Version | 1.1.1 |
| License | GPL-2 |
| Depends | R (>= 2.15.0) |
| Imports | |
| LinkingTo | |
| Suggests | |
| SystemRequirements | |
| Enhances | |
| URL | http://bioinformaticsprb.med.wayne.edu/ |
| Depends On Me | maPredictDSC |
| Imports Me | |
| Suggests Me | |
| Build Report |
Package Archives
Follow Installation instructions to use this package in your R session.
| Package Source | LungCancerACvsSCCGEO_1.1.1.tar.gz |
| Windows Binary | LungCancerACvsSCCGEO_1.1.1.zip |
| Mac OS X 10.6 (Snow Leopard) | LungCancerACvsSCCGEO_1.1.1.tgz |
| Mac OS X 10.9 (Mavericks) | LungCancerACvsSCCGEO_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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