ClusterSculptor: Software for expert-steered classification of single particle mass spectra

Abstract

To take full advantage of the vast amount of highly detailed data acquired by single particle mass spectrometers requires that the data be organized according to some rules that have the potential to be insightful. Most commonly cluster analysis methods are used to classify the individual particle mass spectra on the basis of their similarity. Cluster analysis is a powerful strategy for the exploration of high-dimensional data in the absence of a-priori hypotheses or data classification models. However, more often than not, the examination of the data clustering results reveals that many clusters contain particles of different types and that many particles of one type end up in a number of separate clusters. Our experience with cluster analysis shows that we have a vast amount of non-compiled knowledge and intuition that if brought to bear in this effort has the potential to greatly improve it. ClusterSculptor is software package designed to provide a comprehensive and intuitive visual framework to aid scientists introduce their vast knowledge into the data classification process. ClusterSculptor offers a wide variety of tools that are necessary for a high-dimensional, expert-driven activity we call cluster sculpting. ClusterSculptor is designed to be coupled to SpectraMiner, our data mining and visualization software package. The data are first visualized with SpectraMiner and identified problems are exported to ClusterSculptor, where the user steers the reclassification and recombination of clusters of tens of thousands of particle mass spectra in real-time. The resulting sculpted clusters can be then imported back into SpectraMiner. Here we present the results of a study, in which ClusterSculptor is used to classify a complex dataset that includes single particle mass spectra of a variety of particle types. The compositions of these laboratory generated particles were carefully chosen to test some of the more difficult aspects of single particle mass spectroscopy. We demonstrate the use of ClusterSculptor to greatly improve chemical speciation of single particles by introducing expert input into data classification process.