Dataset Information

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

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.

SUBMITTER: Haraszti T

PROVIDER: S-EPMC2891742 | biostudies-literature | 2010 Jun

REPOSITORIES: biostudies-literature

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Publications

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

Haraszti Tamás T Grunze Michael M Anderson Michael G MG

Chemistry Central journal 20100604

<h4>Background</h4>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.<h4>Results</h4>We have implemented a set of functions ...[more]

PMID: 20525317

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Project description:BackgroundPrimary cilia frequency and length are key metrics in studies of ciliogenesis and ciliopathies. Typically, quantitative cilia analysis is done manually, which is very time-consuming. While some open-source and commercial image analysis software applications can segment input data, they still require the user to optimize many parameters, suffer from user bias, and often lack rigorous performance quality assessment (e.g., false positives and false negatives). Further, optimal parameter combinations vary in detection accuracy depending on cilia reporter, cell type, and imaging modality. A good automated solution would analyze images quickly, robustly, and adaptably-across different experimental data sets-without significantly compromising the accuracy of manual analysis.MethodsTo solve this problem, we developed a new software for automated cilia detection in cells (ACDC). The software operates through four main steps: image importation, pre-processing, detection auto-optimization, and analysis. From a data set, a representative image with manually selected cilia (i.e., Ground Truth) is used for detection auto-optimization based on four parameters: signal-to-noise ratio, length, directional score, and intensity standard deviation. Millions of parameter combinations are automatically evaluated and optimized according to an accuracy 'F1' score, based on the amount of false positives and false negatives. Afterwards, the optimized parameter combination is used for automated detection and analysis of the entire data set.ResultsThe ACDC software accurately and adaptably detected nuclei and primary cilia across different cell types (NIH3T3, RPE1), cilia reporters (AcTub, Smo-GFP, Arl13b), and image magnifications (60×, 40×). We found that false-positive and false-negative rates for Arl13b-stained cilia were 1-6%, yielding high F1 scores of 0.96-0.97 (max. = 1.00). The software detected significant differences in mean cilia length between control and cytochalasin D-treated cell populations and could monitor dynamic changes in cilia length from movie recordings. Automated analysis offered up to a 96-fold speed enhancement compared to manual analysis, requiring around 5 s/image, or nearly 18,000 cilia analyzed/hour.ConclusionThe ACDC software is a solution for robust automated analysis of microscopic images of ciliated cells. The software is extremely adaptable, accurate, and offers immense time-savings compared to traditional manual analysis.

Dataset Information

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

Background

Results

Conclusion

Publications

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

Similar Datasets

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