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元学习在异质单细胞实验中发现新的细胞类型
作者:小柯机器人 发布时间:2020/10/22 13:56:19

美国斯坦福大学Jure Leskovec研究团队的最新研究提出了在单细胞RNA测序数据集中发现新的细胞类型的元学习(meta-learning)算法。相关论文发表在2020年10月19日的《自然—方法学》杂志上。

课题组介绍了MARS,这是一种用于识别和注释已知以及新细胞类型的元学习方法。MARS通过跨多个数据集传输潜在细胞表示,克服了细胞类型的异质性。MARS使用深度学习学习细胞嵌入方程以及细胞嵌入空间中一系列特征。该方法具有发现以前从未见过的细胞类型并注释尚未注释的实验的独特能力。

课题组人员应用MARS到一个大的老鼠细胞图谱,显示了MARS在即使从未见过该类细胞情况下也能准确地识别细胞类型的能力。此外,MARS能够自动生成在嵌入空间就概率定义的一个新细胞类型的可解释的名字。

据悉,尽管科学家在细胞种类注释问题上已投入了巨大工作,但从异质性的单细胞RNA测序数据中发现从前未表征过的细胞种类仍然是一个挑战。

附:英文原文

Title: MARS: discovering novel cell types across heterogeneous single-cell experiments

Author: Maria Brbi, Marinka Zitnik, Sheng Wang, Angela O. Pisco, Russ B. Altman, Spyros Darmanis, Jure Leskovec

Issue&Volume: 2020-10-19

Abstract: Although tremendous effort has been put into cell-type annotation, identification of previously uncharacterized cell types in heterogeneous single-cell RNA-seq data remains a challenge. Here we present MARS, a meta-learning approach for identifying and annotating known as well as new cell types. MARS overcomes the heterogeneity of cell types by transferring latent cell representations across multiple datasets. MARS uses deep learning to learn a cell embedding function as well as a set of landmarks in the cell embedding space. The method has a unique ability to discover cell types that have never been seen before and annotate experiments that are as yet unannotated. We apply MARS to a large mouse cell atlas and show its ability to accurately identify cell types, even when it has never seen them before. Further, MARS automatically generates interpretable names for new cell types by probabilistically defining a cell type in the embedding space. 

DOI: 10.1038/s41592-020-00979-3

Source: https://www.nature.com/articles/s41592-020-00979-3

期刊信息

Nature Methods:《自然—方法学》,创刊于2004年。隶属于施普林格·自然出版集团,最新IF:28.467
官方网址:https://www.nature.com/nmeth/
投稿链接:https://mts-nmeth.nature.com/cgi-bin/main.plex