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BioinformaticsBioinformatics
Peer-reviewed journal article

Automated, image-based quantification of peroxisome characteristics with perox-per-cell

Maxwell Lewis NealiD, Nandini ShuklaiD, Fred David MastiD, Jean‐Claude FarréiD, Therese M. PacioiD, Katelyn E. Raney-PlourdeiD, Sumedh Prasad, Suresh Ellappa SubramaniiD, John D. AitchisoniD

Abstract

perox-per-cell automates cumbersome, image-based data collection tasks often encountered in peroxisome research. The software processes microscopy images to quantify peroxisome features in yeast cells. It uses off-the-shelf image processing tools to automatically segment cells and peroxisomes and then outputs quantitative metrics including peroxisome counts per cell and spatial areas. In validation tests, we found that perox-per-cell output agrees well with manually-quantified peroxisomal counts and cell instances, thereby enabling high-throughput quantification of peroxisomal characteristics. The software is available at https://github.com/AitchisonLab/perox-per-cell

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02 /Specification & Classification

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Citations
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1.16
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top 24%

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