Current cell-type annotation tools for single-cell RNA sequencing (scRNA-seq) data mainly utilize well-annotated source data to help identify cell types in target data. However, on account of privacy preservation, their requirements for raw source data may not always be satisfied. In this case, achieving feature alignment between source and target data explicitly is impossible. Additionally, these methods are barely able to discover the presence of novel cell types. A subjective threshold is often selected by users to detect novel cells. We propose a universal annotation framework for scRNA-seq data called scEMAIL(Expert enseMble novel cell-type perception and local Affinity constraInts of muLti-order for scRNA-seq data), which automatically detects novel cell types without accessing source data during adaptation. For new cell type identification, a novel cell-type perception module is designed with three steps. First, an expert ensemble system measures uncertainty of each cell from three complementary aspects. Second, based on this measurement, bimodality tests are applied to detect the presence of new cell types. Third, once assured of their presence, an adaptive threshold via manifold mixup partitions target cells into “known” and “unknown” groups. Model adaptation is then conducted to alleviate the batch effect. We gather multi-order neighborhood messages globally and impose local affinity regularizations on “known” cells. These constraints mitigate wrong classifications of the source model via reliable self-supervised information of neighbors. scEMAIL is accurate and robust under various scenarios in both simulation and real data. It is also flexible to be applied to challenging single-cell ATAC-seq data without loss of superiority.

邓明华，现任北京大学数学科学学院教授及概率统计系副系主任，北京大学定量生物学中心教授，曾任美国耶鲁大学医学院流行病与公共卫生系访问副教授。已发表70多篇SCI论文。先后主持国家自然科学基金面上项目5项，主持科技部863项目1项，参加科技部973项目3项，参加国家自然科学基金创新团队2项，合作主持国家自然科学基金海外及港澳学者合作研究项目1项，参加科技部重点研发项目1项