OOMPA is an object-oriented microarray and proteomics analysis library implemented in R using S4 classes and compatible with BioConductor.
OOMPA includes experimental versions of two new packages:
- ArrayCube: builds on fundamental classes from BioConductor to define a structure that generalizes the MINiML format used at the Gene Expression Omnibus. The main enhancement over MINiML format is the inclusion of an annotated data frame containing sample characteristics. The package provides routines to convert an ArrayCube into either an AffyBatch or an RGList, as appropriate.
- MINiML: reads files in the MINiML format, as downloaded from the Gene Expression Omnibus, and stores them in R as ArrayCubes.
Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center
- R package
:: MORE INFORMATION
Development of a robust classifier for quality control of reverse-phase protein arrays.
Ju Z, Liu W, Roebuck PL, Siwak DR, Zhang N, Lu Y, Davies MA, Akbani R, Weinstein JN, Mills GB, Coombes KR.
Bioinformatics. 2014 Nov 6. pii: btu736.
SIBER: systematic identification of bimodally expressed genes using RNAseq data.
Tong P, Chen Y, Su X, Coombes KR.
Bioinformatics. 2013 Mar 1;29(5):605-13. doi: 10.1093/bioinformatics/bts713. Epub 2013 Jan 9
integIRTy: a method to identify genes altered in cancer by accounting for multiple mechanisms of regulation using item response theory.
Tong P, Coombes KR.
Bioinformatics. 2012 Nov 15;28(22):2861-9. doi: 10.1093/bioinformatics/bts561
Cancer Inform. 2009 Aug 5;7:199-216.
The bimodality index: a criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data.
Wang J, Wen S, Symmans WF, Pusztai L, Coombes KR.