Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays

Kevin Smith, Filippo Piccinini, Tamas Balassa, Krisztian Koos, Tivadar Danka, Hossein Azizpour, Peter Horvath

Research output: Contribution to journalReview article

Abstract

Phenotypic image analysis is the task of recognizing variations in cell properties using microscopic image data. These variations, produced through a complex web of interactions between genes and the environment, may hold the key to uncover important biological phenomena or to understand the response to a drug candidate. Today, phenotypic analysis is rarely performed completely by hand. The abundance of high-dimensional image data produced by modern high-throughput microscopes necessitates computational solutions. Over the past decade, a number of software tools have been developed to address this need. They use statistical learning methods to infer relationships between a cell's phenotype and data from the image. In this review, we examine the strengths and weaknesses of non-commercial phenotypic image analysis software, cover recent developments in the field, identify challenges, and give a perspective on future possibilities.

Original languageEnglish
Pages (from-to)636-653
Number of pages18
JournalCell Systems
Volume6
Issue number6
DOIs
Publication statusPublished - Jun 27 2018

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    Smith, K., Piccinini, F., Balassa, T., Koos, K., Danka, T., Azizpour, H., & Horvath, P. (2018). Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays. Cell Systems, 6(6), 636-653. https://doi.org/10.1016/j.cels.2018.06.001