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

Cell Syst. 2018 Jun 27;6(6):636-653. doi: 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 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.

Keywords: cell classification; drug screening; freely available tools; high-content screening; machine learning; microscopy; oncology; phenomics; phenotypic image analysis; single-cell analysis.

Publication types

  • Research Support, Non-U.S. Gov't
  • Review

MeSH terms

  • Animals
  • Big Data
  • High-Throughput Screening Assays / methods*
  • Humans
  • Image Processing, Computer-Assisted / methods*
  • Machine Learning
  • Microscopy / methods
  • Phenotype
  • Software