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Updated: May 17, 2026

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Computational methods for evaluation of cell-based data assessment--Bioconductor.

Nolwenn Le Meur1

  • 1Department of Epidemiology and Biostatistics, EHESP, F-35043 Rennes, France. nlemeur@gmail.com

Current Opinion in Biotechnology
|October 16, 2012
PubMed
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This review highlights R and Bioconductor packages for analyzing high-throughput flow cytometry (FCM) data. These tools address challenges in automation, standardization, and reproducibility for cell-based assays.

Area of Science:

  • Computational Biology
  • Bioinformatics
  • Data Science

Background:

  • High-throughput screening (HTS) using cell-based assays is advancing rapidly due to miniaturization and automation.
  • This progress introduces significant challenges in managing and analyzing the generated data.
  • Automation, standardization, and reproducibility are now critical for high-quality research in this field.

Purpose of the Study:

  • To review principal R and Bioconductor packages for flow cytometry (FCM) data analysis.
  • To highlight the advantages and limitations of these open-source tools.
  • To provide an overview of the current landscape for handling complex FCM datasets.

Main Methods:

  • The review focuses on R and Bioconductor packages designed for FCM data analysis.

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Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
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Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

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Last Updated: May 17, 2026

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces
12:04

A High-throughput Cell Microarray Platform for Correlative Analysis of Cell Differentiation and Traction Forces

Published on: March 1, 2017

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software
09:57

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Published on: December 16, 2014

  • Key workflow steps covered include data management, quality assessment, normalization, outlier detection, automated gating, cluster labeling, and feature extraction.
  • The open-source nature of R and Bioconductor facilitates continuous improvement and development.

    • Main Results:

    • Several R and Bioconductor packages are available to manage and analyze FCM data.
    • These packages collectively address major steps in the FCM analysis workflow.
    • The open-source ecosystem fosters ongoing advancements, particularly in clustering and data mining.

    Conclusions:

    • R and Bioconductor offer a robust suite of tools for high-throughput flow cytometry data analysis.
    • These packages are crucial for achieving automation, standardization, and reproducibility in cell-based assays.
    • The continuous development within the R/Bioconductor community ensures evolving solutions for complex data mining challenges.