Robust Peak Detection and Alignment of nanoLC-FT Mass Spectrometry Data

In liquid chromatography-mass spectrometry (LC-MS) based expression proteomics, samples from different groups are analyzed comparatively in order to detect differences that can possibly be caused by the disease under study (potential biomarker detection). To this end, advanced computational techniques are needed. Peak alignment and detection are two key steps in the analysis process of LC-MS datasets. In this paper we propose an algorithm for LC-MS peak detection and alignment. The goal of the algorithm is to group together peaks generated by the same peptide but detected in different samples. It employs clustering with a new weighted similarity measure and automatic selection of the number of clusters. Moreover, it supports parallelization by acting on blocks. Finally, it allows incorporation of available domain knowledge for constraining and refining the search for aligned peaks. Application of the algorithm to a LC-MS dataset generated by a spike-in experiment substantiates the effectiveness of the proposed technique.