Metabolomic data deconvolution using probabilistic purification models

Liquid (or gas) chromatography coupled with mass spectrometry (LC-MS or GC-MS) allows quantitative comparison of biomolecular abundance in biological samples to help with the discovery of candidate biomarkers for complex diseases such as cancer. A fundamental challenge in using quantitative analysis of biomolecules by LC-MS or GC-MS for cancer biomarker discovery is owing to the heterogeneous nature of human biospecimens. Various contaminations present in cancerous tissues or adjacent non-cancerous constituents confound the characterization of molecular expression profiles and thus hinder the discovery of reliable biomarkers. We previously applied probabilistic purification model on a relatively small sample-size metabolomic data. In this study, we further apply probabilistic purification models on larger sample-size and multi-group metabolomic datasets acquired by analysis of liver tissues using both LC-MS and GC-MS. We demonstrate the advantages of incorporating purification models in retrieving underlying sources and reduce noise in metabolomic data. Furthermore, we investigate the benefit of the proposed models in improving our ability to detect changes in the level of metabolites among liver tissue from multiple groups (tumor, liver cirrhosis, and normal).