Mass spectrometry quality control: Instrument monitoring and pattern mining insights Introduction Because of the inherent complexity of mass spectrometry, the results of an experiment can be subject to a large variability. In order to assess the quality of a mass spectrometry experiment, several different quality control metrics have been defined. These metrics can be computed from the experimental spectral-derived data, and they capture the important operational characteristics of a mass

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... ometer. Additionally, besides these metrics based on the experimental data, there is a separate part of qualitative information available in the form of the mass spectrometer instrument parameters. This information provides complementary insights into the operational characteristics of a mass spectrometer compared to the aforementioned quality control metrics that originate from the mass spectral data. Methods The core of the instrument monitoring functionality consists of a central database that is optimized to store a vast amount of instrument parameters, called the iMonDB (Instrument MONitoring DataBase). Software tools exist to set up and populate the database in an automated fashion, and to visualize the instrument parameters stored in the iMonDB to perform a downstream analysis. Such structured recording of instrument parameters results in a large set of data detailing the instrument performance over time. Because of the high-dimensional nature of this data, advanced techniques are required to interpret it. For example, pattern mining algorithms that take into account the relationships between various parameters, such as subspace clustering, can be employed to analyze the high-dimensional data. Preliminary Data For each particular instrument the iMonDB contains the instrument parameters that were in effect during the execution of the experiments performed on the instrument. Possible parameters are for example the status of the ion source, the [...]