pixels with the same coordinates) in the individual images thus correspond to the same position on the sample. The different images are assumed to represent compositional maps of the same sample area or volume, respectively. Instead, ScatterJn focuses on qualitative analysis and exploration of datasets that consist of more than two images. These are very intricate methods for evaluating pairwise image data, which are not implemented in ScatterJn. Apart from generating scatterplots, this tool allows for the calculation of statistical indicators such as the Pearson’s coefficient, Manders’ coefficients, and Li’s intensity correlation quotient as well as statistical significance testing. With our tool, we complement existing methods by providing an additional way of data exploration and analysis.Ī number of software tools exist that allow for colocalization analysis of image data, most notably the Fiji plugin Coloc 2 ( ). While the generation of scatterplot matrices combined with interactive functions is a common tool for visualizing multi-dimensional datasets, to our knowledge it has not yet been applied to image data. Our tool, ScatterJn, creates a matrix of 2D scatterplots, which allows for extending this approach to datasets consisting of more than 2 or 3 images. Because the scatterplots have to be displayed in a coordinate system, this type of analysis is usually limited to datasets consisting of no more than 2 (in a few cases 3) images. This can be combined with tracing back features in the scatterplot (e.g. Displaying image data in scatterplots provides an intuitive view on properties such as variations, correlations, trends, and clustering. Each of these methods deals with particular aspects of the data and has its specific shortcomings and advantages.Īn additional, very useful approach that is often applied to analytical microscopy datasets is an analysis based on 2-dimensional histograms or so-called scatterplots. These include direct visual display in colour-coded overlay images calculation of statistical indicators and various types of cluster analysis, which aim at detecting areas of similar composition. A number of evaluation methods exist to extract part of this information. The information contained in such datasets is sometimes difficult to access. These methods typically produce data in the form of sets of images (or image stacks in 3D case) that each represent the spatial distribution e.g. Methods that allow for the spatially resolved chemical analysis of samples on the micron and submicron scales include scanning electron microscopy (SEM) and transmission electron microscopy (TEM), both in combination with energy-dispersive X-ray spectroscopy (EDX) mapping, nanoscale secondary ion mass spectrometry (nanoSIMS), scanning transmission X-ray microscopy (STXM), and confocal laser scanning microscopy (CLSM). From spatially resolved chemical information of samples such as elemental or species maps, it is possible to derive information about reaction mechanisms and processes.
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