小柯机器人

德国慕尼黑工业大学Fabian J. Theis等研究人员合作发现，单细胞数据集的群体级整合可实现跨样本的多尺度分析。2023年10月9日，《自然—方法学》杂志在线发表了这项成果。

研究人员报道了单细胞群体级整合（scPoli），这是一种开放世界学习器，它结合生成模型来学习样本和细胞表征，以进行数据整合、标签转移和参考映射。研究人员将scPoli应用于肺细胞和外周血单核细胞的群体级图谱，后者由2375个样本中的780万个细胞组成。研究人员证明，scPoli可以利用样本嵌入解释样本级生物和技术差异，从而揭示与批次效应和生物效应相关的基因。scPoli还适用于转座酶染色质可及性和跨物种数据集的单细胞测序分析，为染色质可及性和比较基因组学提供见解。

研究人员设想，scPoli将成为群体级单细胞数据整合的重要工具，不仅能促进图集的使用，还能通过多尺度分析进行解释。

据介绍，越来越多的群体级单细胞图集有可能将样本元数据与细胞数据联系起来。构建这种参考需要整合具有不同元数据的异质队列。

附：英文原文

Title: Population-level integration of single-cell datasets enables multi-scale analysis across samples

Author: De Donno, Carlo, Hediyeh-Zadeh, Soroor, Moinfar, Amir Ali, Wagenstetter, Marco, Zappia, Luke, Lotfollahi, Mohammad, Theis, Fabian J.

Issue&Volume: 2023-10-09

Abstract: The increasing generation of population-level single-cell atlases has the potential to link sample metadata with cellular data. Constructing such references requires integration of heterogeneous cohorts with varying metadata. Here we present single-cell population level integration (scPoli), an open-world learner that incorporates generative models to learn sample and cell representations for data integration, label transfer and reference mapping. We applied scPoli on population-level atlases of lung and peripheral blood mononuclear cells, the latter consisting of 7.8 million cells across 2,375 samples. We demonstrate that scPoli can explain sample-level biological and technical variations using sample embeddings revealing genes associated with batch effects and biological effects. scPoli is further applicable to single-cell sequencing assay for transposase-accessible chromatin and cross-species datasets, offering insights into chromatin accessibility and comparative genomics. We envision scPoli becoming an important tool for population-level single-cell data integration facilitating atlas use but also interpretation by means of multi-scale analyses.

DOI: 10.1038/s41592-023-02035-2

Source: https://www.nature.com/articles/s41592-023-02035-2

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：47.99
官方网址：https://www.nature.com/nmeth/
投稿链接：https://mts-nmeth.nature.com/cgi-bin/main.plex