近日，加拿大英属哥伦比亚大学Sohrab P. Shah和Kieran R. Campbell等研究人员合作，开发出用于肿瘤微环境分析的单细胞RNA-seq的概率细胞类型分配算法。相关论文2019年9月9日在线发表在《自然—方法学》上。

据研究人员介绍，单细胞RNA测序，使复杂组织分解成功能不同的细胞类型。通常，研究人员希望通过无监督聚类，然后通过手工注释或“映射”到现有数据，将细胞分配至不同细胞类型。然而，手工注释对大型数据集的扩展性很差，而映射方法需要纯化或预先注释的数据，并且两者都易于产生批次效应。

为了克服这些问题，研究人员提出了CellAssign算法，这是一种概率模型，利用细胞类型标记基因的先前知识，将单细胞RNA测序数据注释为预定义或新细胞类型、CellAssign以高度可扩展的方式跨大型数据集自动处理分配细胞的过程，同时控制批次和样本效应。研究人员通过对高级别浆液性卵巢癌和滤泡性淋巴瘤中肿瘤微环境组成的广泛模拟和分析，证明了CellAssign的优势。

附：英文原文

Title: Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling

Author: Allen W. Zhang, Ciara OFlanagan, Elizabeth A. Chavez, Jamie L. P. Lim, Nicholas Ceglia, Andrew McPherson, Matt Wiens, Pascale Walters, Tim Chan, Brittany Hewitson, Daniel Lai, Anja Mottok, Clementine Sarkozy, Lauren Chong, Tomohiro Aoki, Xuehai Wang, Andrew P Weng, Jessica N. McAlpine

Issue&Volume: 2019-09-09

Abstract: Single-cell RNA sequencing has enabled the decomposition of complex tissues into functionally distinct cell types. Often, investigators wish to assign cells to cell types through unsupervised clustering followed by manual annotation or via mapping to existing data. However, manual interpretation scales poorly to large datasets, mapping approaches require purified or pre-annotated data and both are prone to batch effects. To overcome these issues, we present CellAssign, a probabilistic model that leverages prior knowledge of cell-type marker genes to annotate single-cell RNA sequencing data into predefined or de novo cell types. CellAssign automates the process of assigning cells in a highly scalable manner across large datasets while controlling for batch and sample effects. We demonstrate the advantages of CellAssign through extensive simulations and analysis of tumor microenvironment composition in high-grade serous ovarian cancer and follicular lymphoma. CellAssign uses a probabilistic model to assign single cells to a given cell type defined by known marker genes, enabling automated annotation of cell types present in a tumor microenvironment.

DOI: 10.1038/s41592-019-0529-1

Source:https://www.nature.com/articles/s41592-019-0529-1