A comprehensive approach to computational mass spectrometry quality control Wout Bittremieux, Hanny Willems, Lennart Martens, Bart Goethals, Dirk Valkenborg and Kris Laukens biomina Pattern mining Due to the curse of dimensionality, specialized data mining techniques are required to detect patterns in the high-dimensional quality control data. Subspace mining: • Find a suitable subset of the original feature space by disregarding irrelevant dimensions • Within each subspace: clustering, outlier detection, … Subspace clustering: 1. Subspace detection: Cartification4 & frequent itemset mining5 2. Clustering in the detected (low-dimensional) subspaces Detected subspaces: • Related metrics: significant overlap with previous manually defined groups of co-occurring metrics • New relationships between metrics to be validated using expert knowledge Detected clusters: • Highly dependent on projected subspaces • Capture valid relationships between experiments (Figure 2) • Indicate faulty experimental results (Figure 3) Figure 2: Subspace clustering is able to detect rela5onships between metrics. For example, experiments that have a higher number of MS1 scans due to a higher MS1 collec5on frequency are clustered together. Figure 3: Clusters can be linked to instrument performance. For example, the highlighted cluster indicates experiments where no MS2 scans were collected due to a broken S-­‐lens. The awareness has risen that suitable quality control informa5on is mandatory to assess the validity of a mass spectrometry experiment. A complementary source of qualita5ve informa5on is available in the form of the mass spectrometer instrument seHngs, which provide addi5onal insights into the opera5onal characteris5cs of a mass spectrometer. Furthermore, we have developed specialized paJern mining algorithms to interpret this high-­‐dimensional data. The derived paJerns could subsequently be used to op5mize experimental design and mass spectrometry instrument seHngs. Conclusion Biomedical Informatics Research Center Antwerp (biomina) & Advanced Database Research and Modeling (ADReM) research group Department of Mathematics and Computer Science, University of Antwerp / Antwerp University Hospital, Belgium 1. Rudnick et al. (2010) Molecular & Cellular Proteomics 9:225–241. 2. Ma et al. (2012) Analytical Chemistry 84:5845–5850. 3. Bittremieux et al. In preparation. 4. Aksehirli et al. (2013) ICDM ’13 937–942. 5. Naulaerts et al. (2013) Briefings in Bioinformatics. References wout.biJremieux@uantwerpen.be Instrument monitoring Advantages of monitoring instrument settings: • Can be directly related to the part of the instrument that is malfunctioning • Highly sensitive ➞ Small differences in instrument behavior will be rapidly detected Logging of external events that occurred to the mass spectrometer instrument: • E.g. calibrations, (periodic) maintenance, unexpected incidents, … • Vital information when interpreting the evolution of the instrument settings over the course of multiple experiments iMonDB: Instrument MONitoring DataBase • Tools to extract instrument settings from experimental raw files and store these in a database for longitudinal analysis • Tools to visualize instrument settings and to record structured information about external events (Figure 1) • API for use by developers • Tools, documentation and source code released as open source and freely available from: https://bitbucket.org/proteinspector/jmondb/ Figure 1: Instrument behavior can be monitored over 5me and can be related to external events. For example, the turbo pump of the mass spectrometer broke down, resul5ng in an increased power consump5on, and had to be replaced. Mass spectrometry quality control Quality control data = standardized quality control samples (e.g. BSA samples): • Extensive frequency: Periodically ran before, during, and after experimental samples ➞ Detect problems as soon as possible • Low complexity ➞ Limited variability Two sources of quality contro