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新方法用最优传输指标推断单细胞数据的基因轨迹
作者:小柯机器人 发布时间:2024/4/10 14:58:56

美国耶鲁大学Yuval Kluger研究小组用最优传输指标推断单细胞数据的基因轨迹。2024年4月5日,《自然—生物技术》杂志在线发表了这项成果。

研究人员表示,单细胞RNA测序已被广泛用于研究生物过程的细胞状态转换和基因动态。目前推断过程中基因序列动态的策略通常依赖于通过细胞轨迹推断构建细胞伪时间。然而,同一组细胞中同时存在的基因过程和技术噪音会掩盖所研究过程的真实进展。

为了应对这一挑战,研究人员提出了基因轨迹(GeneTrajectory),这是一种识别基因轨迹而非细胞轨迹的方法。具体来说,通过计算整个细胞-细胞图中基因分布之间的最佳传输距离,来提取基因程序并定义其基因伪时序。研究人员证明GeneTrajectory能准确提取髓系成熟过程中的渐进基因动态。

此外,研究人员还证明GeneTrajectory分解了小鼠皮肤毛囊真皮凝聚分化的关键基因程序,而这些程序是细胞轨迹方法无法解决的。GeneTrajectory有助于发现控制生物过程变化和活动的基因程序。

附:英文原文

Title: Gene trajectory inference for single-cell data by optimal transport metrics

Author: Qu, Rihao, Cheng, Xiuyuan, Sefik, Esen, Stanley III, Jay S., Landa, Boris, Strino, Francesco, Platt, Sarah, Garritano, James, Odell, Ian D., Coifman, Ronald, Flavell, Richard A., Myung, Peggy, Kluger, Yuval

Issue&Volume: 2024-04-05

Abstract: Single-cell RNA sequencing has been widely used to investigate cell state transitions and gene dynamics of biological processes. Current strategies to infer the sequential dynamics of genes in a process typically rely on constructing cell pseudotime through cell trajectory inference. However, the presence of concurrent gene processes in the same group of cells and technical noise can obscure the true progression of the processes studied. To address this challenge, we present GeneTrajectory, an approach that identifies trajectories of genes rather than trajectories of cells. Specifically, optimal transport distances are calculated between gene distributions across the cell–cell graph to extract gene programs and define their gene pseudotemporal order. Here we demonstrate that GeneTrajectory accurately extracts progressive gene dynamics in myeloid lineage maturation. Moreover, we show that GeneTrajectory deconvolves key gene programs underlying mouse skin hair follicle dermal condensate differentiation that could not be resolved by cell trajectory approaches. GeneTrajectory facilitates the discovery of gene programs that control the changes and activities of biological processes.

DOI: 10.1038/s41587-024-02186-3

Source: https://www.nature.com/articles/s41587-024-02186-3

期刊信息

Nature Biotechnology:《自然—生物技术》,创刊于1996年。隶属于施普林格·自然出版集团,最新IF:68.164
官方网址:https://www.nature.com/nbt/
投稿链接:https://mts-nbt.nature.com/cgi-bin/main.plex