title: An Overview of data science uses in bioimage informatics.
abstract: This review aims at providing a practical overview of the use of statistical features and associated data science methods in bioimage informatics. To achieve a quantitative link between images and biological concepts, one typically replaces an object coming from an image (a segmented cell or intracellular object, a pattern of expression or localisation, even a whole image) by a vector of numbers. They range from carefully crafted biologically relevant measurements to features learnt through deep neural networks. This replacement allows for the use of practical algorithms for visualisation, comparison and inference, such as the ones from machine learning or multivariate statistics. While originating mainly, for biology, in high content screening, those methods are integral to the use of data science for the quantitative analysis of microscopy images to gain biological insight, and they are sure to gather more interest as the need to make sense of the increasing amount of acquired imaging data grows more pressing.
authors.affiliation: LOB, Ecole Polytechnique, CNRS, INSERM, Université Paris-Saclay, 91128 Palaiseau cedex, France. Electronic address: anatole.chessel@polytechnique.edu.
authors.forename: Anatole
authors.initials: A
authors.lastname: Chessel
mesh_terms: D000704:Analysis of Variance; D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods / Q000706:statistics & numerical data*; D033181:Information Dissemination / Q000379:methods; D016247:Information Storage and Retrieval / Q000379:methods; D000069550:Machine Learning; D008856:Microscopy, Fluorescence / Q000706:statistics & numerical data*; D010363:Pattern Recognition, Automated / Q000706:statistics & numerical data*
keywords: Bioimage informatics; Data science; High content screening; Image analysis