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

Kevin Smith1, Filippo Piccinini2, Tamas Balassa3

  • 1KTH Royal Institute of Technology, School of Electrical Engineering and Computer Science, Lindstedtsvägen 3, 10044 Stockholm, Sweden; Science for Life Laboratory, Tomtebodavägen 23A, 17165 Solna, Sweden.

Cell Systems
|June 29, 2018
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Summary
This summary is machine-generated.

Computational tools are essential for analyzing cell properties from microscopic images. This review explores non-commercial software for phenotypic image analysis, highlighting its strengths, weaknesses, and future directions.

Keywords:
cell classificationdrug screeningfreely available toolshigh-content screeningmachine learningmicroscopyoncologyphenomicsphenotypic image analysissingle-cell analysis

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Area of Science:

  • Computational biology
  • Cellular imaging
  • Bioinformatics

Background:

  • Phenotypic image analysis identifies cell property variations from microscopic data.
  • These variations, influenced by gene-environment interactions, are crucial for biological discovery and drug response assessment.
  • Manual analysis is infeasible due to high-dimensional data from high-throughput microscopy.

Purpose of the Study:

  • To review non-commercial phenotypic image analysis software.
  • To discuss recent advancements and challenges in the field.
  • To provide a future outlook on computational phenotypic analysis.

Main Methods:

  • Review of existing non-commercial software tools.
  • Analysis of statistical learning methods used in image analysis.
  • Examination of software strengths and weaknesses.

Main Results:

  • Identified various non-commercial software solutions for phenotypic image analysis.
  • Highlighted the reliance on statistical learning for inferring cell phenotype from image data.
  • Detailed current challenges and recent developments in the field.

Conclusions:

  • Non-commercial software plays a vital role in modern phenotypic image analysis.
  • Continued development is needed to address challenges and leverage future possibilities.
  • Computational approaches are indispensable for analyzing complex cellular phenotypes.