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Systematic data analysis pipeline for quantitative morphological cell phenotyping.

Farzan Ghanegolmohammadi1,2, Mohammad Eslami3, Yoshikazu Ohya2

  • 1Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.

Computational and Structural Biotechnology Journal
|August 6, 2024
PubMed
Summary
This summary is machine-generated.

Quantitative morphological phenotyping (QMP) provides a systematic workflow for analyzing cellular and population-level features. This guide refines QMP methods, offering practical R functions for enhanced phenome studies.

Keywords:
Cell morphologyHigh-content imagingImage-based cell profilingMorphological profile

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Area of Science:

  • Biology
  • Image Analysis
  • Bioinformatics

Background:

  • Quantitative morphological phenotyping (QMP) is an image-based technique for analyzing cellular and population-level features.
  • The interdisciplinary nature of QMP can introduce uncertainties, especially for new researchers.
  • Sophisticated QMP approaches leverage subtle cellular changes for high analytical specificity.

Purpose of the Study:

  • To outline a systematic workflow for refining Quantitative Morphological Phenotyping (QMP) methodology.
  • To provide a practical review of QMP steps, discussing methods, applications, advantages, and disadvantages.
  • To offer R functions and packages for easy implementation of QMP techniques.

Main Methods:

  • Systematic workflow outlining key steps in Quantitative Morphological Phenotyping (QMP).
  • Discussion of various QMP methods, their applications, pros, and cons.
  • Integration of R functions and packages for practical QMP implementation.

Main Results:

  • A refined, systematic workflow for Quantitative Morphological Phenotyping (QMP).
  • Comprehensive overview of QMP methods with practical implementation guidance using R.
  • Identification of advantages and disadvantages for different QMP approaches.

Conclusions:

  • The outlined workflow and R implementation guide aim to reduce uncertainties in QMP.
  • This review facilitates broader application of phenome studies by simplifying QMP.
  • Researchers can achieve more efficient and effective phenome analysis by leveraging established QMP endeavors.