美国马萨诸塞州总医院Luca Pinello与合作者研究提出了SIMBA，这是一种能将单个细胞及其定义特征联合嵌入到一个共同潜在空间的方案。相关研究成果2023年5月29日在线发表于《自然—方法学》杂志上。

据介绍，目前大多数单细胞分析管道仅限于细胞嵌入，严重依赖聚类，同时缺乏明确建模不同特征类型之间交互的能力。此外，这些方法是针对特定任务量身定制的，因为不同的单细胞问题的表述方式不同。

为了克服这些缺陷，研究人员提出了SIMBA，这是一种将单个细胞及其定义特征（如基因、染色质可及区域和DNA序列）联合嵌入公共潜在空间的图嵌入方法。通过利用细胞和特征的共同嵌入，SIMBA允许研究细胞异质性、无聚类标记发现、基因调控推断、批量效应去除和组学数据整合。

总之，研究人员表明，SIMBA提供了一个单一的框架，允许以统一的方式制定各种单细胞问题，从而简化新分析方法的开发和对新单细胞模式的扩展。SIMBA是作为一个全面的Python库实现的（https://simbabio.readthedocs.io)。

附：英文原文

Title: SIMBA: single-cell embedding along with features

Author: Chen, Huidong, Ryu, Jayoung, Vinyard, Michael E., Lerer, Adam, Pinello, Luca

Issue&Volume: 2023-05-29

Abstract: Most current single-cell analysis pipelines are limited to cell embeddings and rely heavily on clustering, while lacking the ability to explicitly model interactions between different feature types. Furthermore, these methods are tailored to specific tasks, as distinct single-cell problems are formulated differently. To address these shortcomings, here we present SIMBA, a graph embedding method that jointly embeds single cells and their defining features, such as genes, chromatin-accessible regions and DNA sequences, into a common latent space. By leveraging the co-embedding of cells and features, SIMBA allows for the study of cellular heterogeneity, clustering-free marker discovery, gene regulation inference, batch effect removal and omics data integration. We show that SIMBA provides a single framework that allows diverse single-cell problems to be formulated in a unified way and thus simplifies the development of new analyses and extension to new single-cell modalities. SIMBA is implemented as a comprehensive Python library (https://simba-bio.readthedocs.io).

DOI: 10.1038/s41592-023-01899-8

Source: https://www.nature.com/articles/s41592-023-01899-8