1. BioProfiling.jl: profiling biological perturbations with high-content imaging in single cells and heterogeneous populations
- Author
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Loan Vulliard, Joel Hancock, Anton Kamnev, Christopher W Fell, Joana Ferreira da Silva, Joanna I Loizou, Vanja Nagy, Loïc Dupré, Jörg Menche, Benson-Rumiz, Alicia, Research Center for Molecular Medicine of the Austrian Academy of Sciences [Vienna, Austria] (CeMM ), Austrian Academy of Sciences (OeAW), University of Vienna [Vienna], Medizinische Universität Wien = Medical University of Vienna, Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases (LBI-RUD), Institut Toulousain des Maladies Infectieuses et Inflammatoires (Infinity), Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre National de la Recherche Scientifique (CNRS), and Faculty of Mathematics [Vienna]
- Subjects
Statistics and Probability ,Computational Mathematics ,[SDV.MHEP] Life Sciences [q-bio]/Human health and pathology ,Computational Theory and Mathematics ,Molecular Biology ,Biochemistry ,[SDV.MHEP]Life Sciences [q-bio]/Human health and pathology ,Computer Science Applications - Abstract
Motivation High-content imaging screens provide a cost-effective and scalable way to assess cell states across diverse experimental conditions. The analysis of the acquired microscopy images involves assembling and curating raw cellular measurements into morphological profiles suitable for testing biological hypotheses. Despite being a critical step, general-purpose and adaptable tools for morphological profiling are lacking and no solution is available for the high-performance Julia programming language. Results Here, we introduce BioProfiling.jl, an efficient end-to-end solution for compiling and filtering informative morphological profiles in Julia. The package contains all the necessary data structures to curate morphological measurements and helper functions to transform, normalize and visualize profiles. Robust statistical distances and permutation tests enable quantification of the significance of the observed changes despite the high fraction of outliers inherent to high-content screens. This package also simplifies visual artifact diagnostics, thus streamlining a bottleneck of morphological analyses. We showcase the features of the package by analyzing a chemical imaging screen, in which the morphological profiles prove to be informative about the compounds' mechanisms of action and can be conveniently integrated with the network localization of molecular targets. Availability and implementation The Julia package is available on GitHub: https://github.com/menchelab/BioProfiling.jl. We also provide Jupyter notebooks reproducing our analyses: https://github.com/menchelab/BioProfilingNotebooks. The data underlying this article are available from FigShare, at https://doi.org/10.6084/m9.figshare.14784678.v2. Supplementary information Supplementary data are available at Bioinformatics online.
- Published
- 2021
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