Integrating multiple single‐cell datasets is essential for the comprehensive understanding of cell heterogeneity. Batch effect is the undesired systematic variations among technologies or experimental laboratories that distort biological signals and hinder the integration of single‐cell datasets. However, existing methods typically rely on a selected dataset as a reference, leading to inconsistent integration performance using different references, or embed cells into uninterpretable low‐dimensional feature space. To overcome these limitations, a reference‐free method, Beaconet, for integrating multiple single‐cell transcriptomic datasets in original molecular space by aligning the global distribution of each batch using an adversarial correction network is presented. Through extensive comparisons with 13 state‐of‐the‐art methods, it is demonstrated that Beaconet can effectively remove batch effect while preserving biological variations and is superior to existing unsupervised methods using all possible references in overall performance. Furthermore, Beaconet performs integration in the original molecular feature space, enabling the characterization of cell types and downstream differential expression analysis directly using integrated data with gene‐expression features. Additionally, when applying to large‐scale atlas data integration, Beaconet shows notable advantages in both time‐ and space‐efficiencies. In summary, Beaconet serves as an effective and efficient batch effect removal tool that can facilitate the integration of single‐cell datasets in a reference‐free and molecular feature‐preserved mode.

中文翻译：

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Beaconet：一种在原始分子空间中整合多批单细胞转录组数据的无参考方法

整合多个单细胞数据集对于全面理解细胞异质性至关重要。批次效应是技术或实验实验室之间不期望的系统变化，它会扭曲生物信号并阻碍单细胞数据集的整合。然而，现有的方法通常依赖于选定的数据集作为参考，导致使用不同参考的集成性能不一致，或者将单元嵌入到无法解释的低维特征空间中。为了克服这些限制，提出了一种无参考方法 Beaconet，通过使用对抗性校正网络对齐每个批次的全局分布，将多个单细胞转录组数据集整合到原始分子空间中。通过与 13 种最先进方法的广泛比较，证明 Beaconet 可以有效消除批次效应，同时保留生物变异，并且在整体性能上优于现有的使用所有可能参考的无监督方法。此外，Beaconet 在原始分子特征空间中进行整合，从而能够直接使用具有基因表达特征的整合数据来表征细胞类型和下游差异表达分析。此外，在应用于大规模图集数据集成时，Beaconet 在时间和空间效率上都表现出显着的优势。总之，Beaconet 作为一种有效且高效的批量效应消除工具，可以促进单细胞数据集在无参考和分子特征保留模式下的集成。