With the rapid development of single-cell sequencing techniques, several large-scale cell atlas projects have been launched across the world. However, it is still challenging to integrate single-cell RNA-seq (scRNA-seq) datasets with diverse tissue sources, developmental stages and/or few overlaps, due to the ambiguity in determining the batch information, which is particularly important for current batch-effect correction methods. Here, we present SCORE, a simple network-based integration methodology, which incorporates curated molecular network features to infer cellular states and generate a unified workflow for integrating scRNA-seq datasets. Validating on real single-cell datasets, we showed that regardless of batch information, SCORE outperforms existing methods in accuracy, robustness, scalability and data integration.

中文翻译：

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通过结合分子网络特征将单细胞数据集与模糊的批次信息相结合

随着单细胞测序技术的飞速发展，全球多个大型细胞图谱项目相继启动。然而，由于确定批次信息的模糊性，整合具有不同组织来源、发育阶段和/或重叠很少的单细胞 RNA-seq (scRNA-seq) 数据集仍然具有挑战性，这对于当前批次尤为重要-效果校正方法。在这里，我们介绍了 SCORE，这是一种简单的基于网络的集成方法，它结合了策划的分子网络特征来推断细胞状态并生成用于集成 scRNA-seq 数据集的统一工作流程。通过对真实的单细胞数据集进行验证，我们表明，无论批次信息如何，SCORE 在准确性、稳健性、可扩展性和数据集成方面都优于现有方法。