A new R package, netClass, has been release. netClass integrate network information, such as protein-protein interaction network or KEGG, to mRNA classification, but also incorporate miRNA to mRNA with mi-mRNA interaction network for biomarker discovery. This methods we called stSVM and already published in PloS ONE (Cun et al 2013). Apart from stSVM, we also implement the flowing methods in netClass:
- AEP (average gene expression of pathway), Guo et al., BMC Bioinformatics 2005, 6:58.
- PAC (pathway activitive classification), Lee E, et al., PLoS Comput Biol 4(11): e1000217.
- hubc (Hub nodes classification), Taylor et al.(2009) Nat. Biotech.: doi: 10.1038/nbt.152
- frSVM (filter via top ranked genes), Cun et al. arXiv:1212.3214 ; Winter etal., PLoS Comput Biol 8(5): e1002511.
- stSVM (network smoothed t-statistic) , Cun et al., PloS One,.
NetClass can be download from souceforg ( http://sourceforge.net/projects/netclassr/) or , CRAN (http://cran.r-project.org/web/packages/netClass/ ). For more detail of netClass, you can refer these four papers:
- Yupeng Cun, Holger Fröhlich (2014) netClass: An R-package for network based, integrative biomarker signature discovery. Bioinformatics; doi: 10.1093/bioinformatics/btu025
- Yupeng Cun, Holger Fröhlich (2013) Network and Data Integration for Biomarker Signature Discovery via Network Smoothed T-Statistics, PLoS ONE 8(9): e73074. doi:10.1371/journal.pone.0073074.
- Yupeng Cun, Holger Fröhlich (2012) Integrating Prior Knowledge Into Prognostic Biomarker Discovery Based on Network Structure. arXiv:1212.3214 ( Link for R codes and Supplemental meterials)
- Yupeng Cun, Holger Fröhlich (2012) Prognostic Gene Signatures for Patient Stratification in Breast Cancer – Accuracy, Stability and Interpretability of Gene Selection Approaches Using Prior Knowledge on Protein-Protein Interactions. BMC Bioinformatics, 13:69
Computational Genomics & Medicine 