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A perspective on FAIR quality control in multiplexed imaging data processing
Wouter-Michiel A M Vierdag1, Sinem K Saka1
1Genome Biology Unit, European Molecular Biology Laboratory (EMBL), Heidelberg, Germany.
Frontiers in Bioinformatics
|February 26, 2024
Summary
Multiplexed imaging generates large datasets, making automated analysis challenging. Integrating rigorous quality control (QC) into image analysis pipelines is crucial for reliable results and data reusability.
Area of Science:
- Bioimage analysis
- Computational pathology
- Digital pathology
Background:
- Multiplexed imaging generates large datasets, posing analysis challenges.
- Technical artifacts and target variability complicate data interpretation.
- Automated pipelines are essential but prone to error propagation.
Purpose of the Study:
- To address challenges in integrating quality control (QC) into multiplexed image analysis pipelines.
- To propose solutions for robust QC in large-scale bioimage analysis.
- To ensure reliable interpretation and reusability of complex imaging data.
Main Methods:
- Review of current multiplexed imaging analysis pipelines.
- Identification of critical steps requiring quality control.
- Exploration of recent advancements in bioimage analysis for QC integration.
Main Results:
- Current frameworks limit interactive QC for large multiplexed datasets.
- Error propagation is a significant issue in sequential analysis steps.
- New QC integration strategies are needed for complex imaging data.
Conclusions:
- Rigorous QC is paramount for accurate analysis and interpretation of multiplexed imaging data.
- Effective QC integration is essential for ensuring data reusability.
- Advances in bioimage analysis offer potential solutions for QC challenges.
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