Instrument-Aware Representation Learning for Robust Cross-Site Flow Cytometry Integration.

Area of Science:

• Biomedical research
• Clinical diagnostics
• Data science

Background:

• Flow cytometry is crucial for research and diagnostics.
• Cross-site analysis is challenging due to instrument and protocol variations.
• Existing data integration methods lack generalizability.

Purpose of the Study:

• To develop a robust data-driven representation learning strategy for cross-site flow cytometry data integration.
• To overcome limitations of current methods in handling diverse instrumentation.
• To enable accurate and generalizable analysis of flow cytometry data across multiple platforms.

Main Methods:

• A probabilistic framework was used to learn compact, instrument-specific embeddings.
• Representation learning was employed to create data-driven models.
• The strategy focused on generalizability to unseen devices and datasets.

Main Results:

• The proposed approach achieved high classification accuracy.
• Data from multiple flow cytometry platforms were effectively merged.
• Inherent biological variation within flow cytometry measurements was preserved.
• The method demonstrated robustness in cross-site data integration.

Conclusions:

• The data-driven representation learning strategy offers a powerful solution for flow cytometry data integration.
• This approach enhances the reliability and generalizability of cross-site analyses.
• It facilitates more accurate biomedical research and clinical diagnostics by harmonizing data from diverse sources.