| Title: | A lung cancer dataset that can be used with maPredictDSC package for developing outcome prediction models from Affymetrix CEL files. |
|---|---|
| Description: | 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). |
| Authors: | Adi Laurentiu Tarca <[email protected]> |
| Maintainer: | Adi Laurentiu Tarca <[email protected]> |
| License: | GPL-2 |
| Version: | 1.48.0 |
| Built: | 2026-10-10 11:06:10 UTC |
| Source: | https://github.com/bioc/LungCancerACvsSCCGEO |
The LungCancerACvsSCCGEO dataset consists: i) a data frame anoLC giving
the file names of the affy cel files used in the training phase and their corresponding
phenotype (AC or SCC) and ii) gsLC the gold standard, i.e. the class membership
of each test sample apearing in anoLC.
data(LC)data(LC)
GEO for the training data, while the test data comes from the citation below:
Adi L. Tarca, Mario Lauria, Michael Unger, Erhan Bilal, Stephanie Boue, Kushal Kumar Dey,
Julia Hoeng, Heinz Koeppl, Florian Martin, Pablo Meyer, Preetam Nandy, Raquel Norel,
Manuel Peitsch, Jeremy J Rice, Roberto Romero, Gustavo Stolovitzky, Marja Talikka,
Yang Xiang, Christoph Zechner, and IMPROVER DSC Collaborators,
Strengths and limitations of microarray-based phenotype prediction: Lessons learned
from the IMPROVER Diagnostic Signature Challenge. Bioinformatics, submitted 2013.