scBatchProx：异质性批次组成下稳定细胞类型可区分性的联邦启发式精炼
scBatchProx: Federated-Inspired Refinement for Stable Cell-Type Discriminability under Heterogeneous Batch Compositions
AI总结：
提出scBatchProx，一种轻量级后处理方法，通过联邦学习启发的优化和保守正则化，稳定单细胞潜在嵌入，提升异质批次下的细胞类型分类性能。
摘要：
单细胞整合工作流通常构建低维细胞嵌入，然后使用后处理方法减少批次效应。当细胞类型组成在不同批次间变化，某些群体在特定批次中代表性不足或缺失时，这种精炼过程可能变得不稳定。在动态单细胞数据系统中，新获取的批次可能改变技术条件和细胞类型组成，问题变得更加严重。这种不稳定性会降低下游细胞类型分类性能，并削弱在失衡扰动下的稳定性。我们引入scBatchProx，一种轻量级后处理方法，用于在这些异质和不断变化的环境中稳定单细胞潜在嵌入。scBatchProx直接操作预计算嵌入，并将每个批次或研究视为联邦启发优化过程中的客户端。批次条件FiLM适配器学习局部潜在更新，而近端和身份保持正则化使这些更新保持保守。在多批次和跨研究单细胞数据集上的实验表明，scBatchProx在不同上游嵌入上改善了下游细胞类型分类。在受控失衡扰动中，当选定群体从一个批次中降采样或移除时，scBatchProx维持更稳定的受影响细胞类型F1分数。在累积重训练和持续整合设置中，随着新数据集随时间到达，scBatchProx保持有效。这些结果共同表明，保守的联邦启发式精炼有助于在批次组成随数据集和时间变化时维持稳定的单细胞嵌入。
英文摘要：
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 performance and weaken stability under imbalance perturbations. We introduce scBatchProx, a lightweight post-hoc refinement method for stabilizing single-cell latent embeddings in these heterogeneous and evolving settings. scBatchProx operates directly on precomputed embeddings and treats each batch or study as a client in a federated-inspired optimization procedure. A batch-conditioned FiLM adapter learns local latent updates, while proximal and identity-preserving regularization keep these updates conservative. Experiments on multi-batch and cross-study single-cell datasets show that scBatchProx improves downstream cell-type classification across different upstream embeddings. In controlled imbalance perturbations, scBatchProx maintains more stable affected-cell-type F1 when selected populations are downsampled or ablated from one batch. In cumulative retraining and continual integration settings, scBatchProx remains effective as new datasets arrive over time. Together, these results suggest that conservative, federated-inspired refinement can help maintain stable single-cell embeddings as batch compositions change across datasets and over time.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
