scBatchProx: Federated-Inspired Refinement for Stable Cell-Type Discriminability under Heterogeneous Batch Compositions
arXiv:2602.00423v3 Announce Type: replace Abstract: Single-cell integration workflows often construct low-dimensional cell embeddings and then refine them with post-hoc methods to reduce batch effects. This refinement process can become unstable when cell-type compositions vary across batches, with some populations underrepresented or absent in particular batches. The problem becomes more consequential in dynamic single-cell data systems, where newly acquired batches can change both technical conditions and cell-type composition. Such instability can reduce downstream cell-type classification
The paper addresses a growing challenge in single-cell data analysis, where heterogeneity across batches, especially in dynamic systems, makes reliable cell-type classification difficult.
This development improves the stability and reliability of single-cell integration workflows, crucial for advancing fields like drug discovery and personalized medicine.
The ability to accurately classify cell types despite variable batch compositions will lead to more robust and trustworthy insights from complex biological data.
- · Biotechnology companies
- · Pharmaceutical research
- · Genomics and proteomics
- · AI in healthcare
- · Traditional batch correction methods
- · Research relying on unstable single-cell analyses
Improved accuracy in identifying specific cell types will accelerate biological research.
More reliable single-cell insights could lead to novel therapeutic targets and diagnostics.
The enhanced data quality might facilitate the development of more sophisticated AI models for disease prediction and treatment.
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Read at arXiv cs.LG