Null Space LDA Based Feature Extraction of Mass Spectrometry Data for Cancer Classification

Early detection of cancer is crucial for successful treatments. High throughput and high resolution mass spectrometry are increasingly used for disease classification. In this paper a novel cancer classification method called Null space based linear discriminant analysis (NS-LDA) is proposed. NSLDA first extracts the first order derivative information of the mass spectrometry profiles. Based on the null-space strategy, NSLDA then reduce the dimension of data and extracts the discriminant features simultaneously. The method was tested and evaluated on the ovarian cancer database OC-WCX2a and prostate cancer database PC-H4. The experimental results on these two real life cancer database show that the NS-LDA method outperforms the PCA and LDA method in the analysis of mass spectrometry data.