Spatialstein: An Open-Source Workflow for Annotation, Deconvolution, and Spatially Aware Segmentation of Mass Spectrometry Imaging Data.

ABSTRACT: Mass Spectrometry Imaging (MSI) data sets are markedly different from optical images. However, analysis algorithms often overlook the intricacies of this kind of data. In MSI, a frequently observed phenomenon is variability in signal intensity between pixels caused by factors other than differences in analyte concentrations. Another common issue is the presence of ions with overlapping isotopic envelopes resulting in isobaric interference. The first factor causes random variations of the signal from the same anatomical regions. The second can cause the spatial distribution of a single peak to represent a mixture of spatial distributions of several analytes. Both factors affect the accuracy of data analysis methods such as MSI segmentation. In this article, we demonstrate that accounting fo

SUBMITTER: Ciach MA 

PROVIDER: S-EPMC12809658 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature