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STXMPy: a new software package for automated region of interest selection and statistical analysis of XANES data

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Haraszti,  Tamas
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;
Biophysical Chemistry, Institute of Physical Chemistry, University of Heidelberg, 69120 Heidelberg, Germany;

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Grunze,  Michael
Cellular Biophysics, Max Planck Institute for Medical Research, Max Planck Society;

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Citation

Haraszti, T., Grunze, M., & Anderson, M. G. (2010). STXMPy: a new software package for automated region of interest selection and statistical analysis of XANES data. Central European Journal of Chemistry, 4(4): 11, pp. 1-8. doi:10.1186/1752-153X-4-11.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0010-3BBA-C
Abstract
BACKGROUND: Soft X-ray spectromicroscopy based absorption near-edge structure analysis, is a spectroscopic technique useful for investigating sample composition at a nanoscale of resolution. While the technique holds great promise for analysis of biological samples, current methodologies are challenged by a lack of automatic analysis software e. g. for selection of regions of interest and statistical comparisons of sample variability. RESULTS: We have implemented a set of functions and scripts in Python to provide a semiautomatic treatment of data obtained using scanning transmission X-ray microscopy. The toolkit includes a novel line-by-line absorption conversion and data filtering automatically identifying image components with significant absorption. Results are provided to the user by direct graphical output to the screen and by output images and data files, including the average and standard deviation of the X-ray absorption spectrum. Using isolated mouse melanosomes as a sample biological tissue, application of STXMPy in analysis of biological tissues is illustrated. CONCLUSION: The STXMPy package allows both interactive and automated batch processing of scanning transmission X-ray microscopic data. It is open source, cross platform, and offers rapid script development using the interpreted Python language.