2 research outputs found

    Hyperspectral Visualization of Mass Spectrometry Imaging Data

    No full text
    The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively

    Hyperspectral Visualization of Mass Spectrometry Imaging Data

    No full text
    The acquisition of localized molecular spectra with mass spectrometry imaging (MSI) has a great, but as yet not fully realized, potential for biomedical diagnostics and research. The methodology generates a series of mass spectra from discrete sample locations, which is often analyzed by visually interpreting specifically selected images of individual masses. We developed an intuitive color-coding scheme based on hyperspectral imaging methods to generate a single overview image of this complex data set. The image color-coding is based on spectral characteristics, such that pixels with similar molecular profiles are displayed with similar colors. This visualization strategy was applied to results of principal component analysis, self-organizing maps and t-distributed stochastic neighbor embedding. Our approach for MSI data analysis, combining automated data processing, modeling and display, is user-friendly and allows both the spatial and molecular information to be visualized intuitively and effectively
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