美国纽约大学基因组学与系统生物学中心Rahul Satija团队报道了综合、多模式和可扩展的单细胞分析方法。该项研究成果发表在2023年5月25日出版的《自然—生物技术》上。

他们介绍了“桥式集成”，这是一种使用多组数据集作为分子桥梁跨模式集成单细胞数据集的方法。数据集中的每个单元格构成了“字典”中的一个元素，该元素用于重建单峰数据集并将其转换为共享空间。他们的程序精确整合转录组学数据与独立的单细胞测量的染色质可及性、组蛋白修饰、DNA甲基化和蛋白质水平。此外，他们展示了字典学习如何与素描技术相结合，以提高计算可扩展性，并协调来自测序和细胞计数实验的860万个人类免疫细胞谱。他们的方法在Seurat工具包的第5版中实现，扩大了单细胞参考数据集的实用性，并促进了不同分子模式的比较。

研究人员表示，将单细胞测序图谱映射到综合参考数据集，为无监督分析提供了一个强大的替代方案。然而，大多数参考数据集是由单细胞RNA测序数据构建的，不能用于注释不测量基因表达的数据集。

附：英文原文

Title: Dictionary learning for integrative, multimodal and scalable single-cell analysis

Author: Hao, Yuhan, Stuart, Tim, Kowalski, Madeline H., Choudhary, Saket, Hoffman, Paul, Hartman, Austin, Srivastava, Avi, Molla, Gesmira, Madad, Shaista, Fernandez-Granda, Carlos, Satija, Rahul

Issue&Volume: 2023-05-25

Abstract: Mapping single-cell sequencing profiles to comprehensive reference datasets provides a powerful alternative to unsupervised analysis. However, most reference datasets are constructed from single-cell RNA-sequencing data and cannot be used to annotate datasets that do not measure gene expression. Here we introduce ‘bridge integration’, a method to integrate single-cell datasets across modalities using a multiomic dataset as a molecular bridge. Each cell in the multiomic dataset constitutes an element in a ‘dictionary’, which is used to reconstruct unimodal datasets and transform them into a shared space. Our procedure accurately integrates transcriptomic data with independent single-cell measurements of chromatin accessibility, histone modifications, DNA methylation and protein levels. Moreover, we demonstrate how dictionary learning can be combined with sketching techniques to improve computational scalability and harmonize 8.6 million human immune cell profiles from sequencing and mass cytometry experiments. Our approach, implemented in version 5 of our Seurat toolkit (http://www.satijalab.org/seurat), broadens the utility of single-cell reference datasets and facilitates comparisons across diverse molecular modalities.

DOI: 10.1038/s41587-023-01767-y

Source: https://www.nature.com/articles/s41587-023-01767-y