AI 中文总结
本研究将单细胞批次整合建模为混杂域适应，提出聚合兼容性图整合器扩展ConDo方法，在基准测试中表现最优，兼具特征空间整合优势与良好性能。
AI 中文摘要
批次整合是单细胞基因组学中的核心预处理步骤，需合并不同实验、供体及技术方案产生的数据集，尽管存在普遍的技术批次效应。主流整合方法生成共享低维嵌入，却丢失了下游差异表达、生物标志物等分析依赖的校正后基因表达值；而保留基因的特征空间（表达校正）方法通常对齐批次的边缘表达分布，一旦批次间细胞类型组成不同，即批次效应与生物信号存在混杂时，就存在抹去真实生物变异的风险。我们将单细胞批次整合重新建模为混杂域适应问题，并应用ConDo方法——该方法匹配给定细胞类型注释的条件表达分布，而非边缘分布。为将ConDo的成对源-目标适配器扩展至多批次场景，我们提出一种聚合兼容性图整合器：批次为节点，当批次间共享某一细胞类型时两节点相连，通过拟合一个ConDo适配器，贪婪地将每个最优得分的邻居合并入不断增长的参考批次。在Open Problems批次整合基准测试中，ConDo是最强的特征空间整合器，在6个数据集中的5个上位列特征方法第一；此外，它在总体得分上与深度嵌入方法相当或更优，在6个数据集中的4个上位列所有方法第一，同时返回校正后的表达值而非不透明的嵌入。
英文摘要
Batch integration is a central preprocessing step in single-cell genomics, where datasets collected across experiments, donors, and protocols must be combined despite pervasive technical batch effects. The leading integration methods produce a shared low-dimensional embedding, which discards the corrected gene-expression values that downstream differential-expression, biomarker, and other analyses depend on. The feature-space (expression-correcting) methods that do preserve genes typically align the marginal expression distributions of batches and thereby risk erasing genuine biological variation whenever cell-type composition differs across batches, i.e. whenever batch effect and biological signal are confounded. We recast single-cell batch integration as confounded domain adaptation and apply ConDo, a method that matches conditional expression distributions given the cell-type annotation rather than marginal distributions. To extend ConDo's pairwise source-to-target adapter to the many-batch setting, we introduce an agglomerative compatibility-graph integrator: batches are nodes connected when they share a cell type, and we greedily merge each best-scoring neighbor into a growing reference by fitting one ConDo adapter. On the Open Problems Batch Integration Benchmark, ConDo is the strongest feature-space integrator, ranking first among feature methods on five of six datasets. Furthermore, it is competitive with or better than deep embedding methods on the overall score, ranking first across all methods on four of six datasets while returning corrected expression rather than an opaque embedding.
CommentsAccepted as a non-archival poster at MLCB 2026
