Cell Systems Review Phenotypic Image Analysis Software Tools for Exploring and Understanding Big Image Data from Cell-Based Assays Kevin Smith,1,2 Filippo Piccinini,3 Tamas Balassa,4 Krisztian Koos,4 Tivadar Danka,4 Hossein Azizpour,1,2 and Peter Horvath4,5,* 1KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvagen€ 3, 10044 Stockholm, Sweden 2Science for Life Laboratory, Tomtebodavagen€ 23A, 17165 Solna, Sweden 3Istituto Scientifico Romagnolo per lo Studio e la Cura dei Tumori (IRST) IRCCS, Via P. Maroncelli 40, Meldola, FC 47014, Italy 4Synthetic and Systems Biology Unit, Hungarian Academy of Sciences, Biological Research Center (BRC), Temesva´ ri krt. 62, 6726 Szeged, Hungary 5Institute for Molecular Medicine Finland, University of Helsinki, Tukholmankatu 8, 00014 Helsinki, Finland *Correspondence:
[email protected] https://doi.org/10.1016/j.cels.2018.06.001 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 environ- ment, 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 computa- tional 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.