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

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. The goal of phenotypic image analysis is to recognize variations in cellular properties using image data—either measurements extracted by image analysis software or directly from the raw pixel values. In this review, we describe free and open-source software tools that are currently available for exploring and quantifying phenotypes in image-based cellular assays. We discuss some of the main challenges, current trends, and future research directions in phenotypic image analysis.