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Image-based cell profiling enhancement via data cleaning methods
Arghavan Rezvani1, Mahtab Bigverdi1, Mohammad Hossein Rohban1
1Department of Computer Engineering, Sharif University of Technology, Tehran, Tehran, Iran.
Plos One
|May 4, 2022
Summary
Preprocessing image-based assay data improves drug mechanism identification. Applying data cleaning, outlier detection, and feature correction enhances biological insights from CellProfiler outputs.
Area of Science:
- Computational biology
- High-throughput screening
- Image analysis
Background:
- Image-based assays are cost-effective for high-throughput biological experiments.
- CellProfiler is widely used for image analysis, generating cell features for treatment profiles.
- Errors in CellProfiler pipelines can impact downstream analyses.
Purpose of the Study:
- To evaluate preprocessing approaches for improving CellProfiler-generated profiles.
- To enhance the preservation of meaningful biological information in image-based profiles.
- To optimize the identification of drug mechanisms of action using enhanced profiles.
Main Methods:
- Examined data cleaning techniques.
- Implemented cell-level outlier and toxic drug detection.
- Regressed out cell area from other features to correct for area-dependent measurements.
Main Results:
- Preprocessing steps significantly improved the quality of image-based profiles.
- Enhanced profiles preserved more relevant biological information compared to raw profiles.
- The tested preprocessing methods were time-efficient.
Conclusions:
- Effective preprocessing is crucial for accurate analysis of image-based assay data.
- Optimized profiles facilitate more reliable identification of drug mechanisms.
- Further research can build upon these preprocessing strategies.
