Robust Estimation and Graph-Based Meta Clustering for LC-MS Feature Extraction

There is an increasing interest in the identification and quantification of proteomic markers for biological and medical research. Experimental techniques utilizing liquid chromatography with mass spectrometry (LC- MS) provide one method of directly observing protein expression, but a substantial amount of computational work is needed to go from the raw LC-MS data to a matrix format analogous to gene expression studies in which the columns are samples and rows are proteins. One critical step in this pipeline is the extraction of peptide features from the LC-MS signal data. We present a complete solution to LC-MS feature detection that combines a model-based approach to feature extraction on the MS scans with techniques for robust estimation to build LC-MS features from the individual scans. We show that using our approach, we find significantly more features, more matches, and better correlation between replicated LC-MS experiments than are found using the current state-of-the-art software.