It is a major challenge to integrate single-cell sequencing data across experiments, conditions, batches, time points, and other technical considerations. New computational methods are required that can integrate samples while simultaneously preserving biological information. Here, we propose an unsupervised reference-free data representation, cluster similarity spectrum (CSS), where each cell is represented by its similarities to clusters independently identified across samples. We show that CSS can be used to assess cellular heterogeneity and enable reconstruction of differentiation trajectories from cerebral organoid and other single-cell transcriptomic data, and to integrate data across experimental conditions and human individuals.

中文翻译：

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CSS：单细胞基因组数据的聚类相似谱整合


跨实验、条件、批次、时间点和其他技术考虑因素整合单细胞测序数据是一项重大挑战。需要新的计算方法来整合样本，同时保留生物信息。在这里，我们提出了一种无监督的无参考数据表示，即聚类相似谱（CSS），其中每个细胞由其与跨样本独立识别的聚类的相似性来表示。我们证明 CSS 可用于评估细胞异质性，并能够根据大脑类器官和其他单细胞转录组数据重建分化轨迹，并整合跨实验条件和人类个体的数据。