美国纪念斯隆凯特琳癌症中心Dana Pe’er团队近期取得重要工作进展，他们研究开发出从单细胞数据中监督发现可解释的基因程序的方法。相关研究成果2023年9月21日在线发表于《自然—生物技术》杂志上。

据介绍，因子分析将单细胞基因表达数据分解为一组最小的基因程序，这些程序对应于样本中细胞执行的过程。然而，矩阵分解方法容易出现技术伪影和较差的因子可解释性。

研究人员用Spectra解决了这些问题，Spectra是一种将用户提供的基因程序与检测新程序相结合的算法，这些新程序可以最好地解释表达协变。Spectra结合了现有的基因集和细胞类型标签作为先前的生物信息，明确地对细胞类型进行建模，并使用惩罚函数将输入基因集表示为基因-基因知识图，以引导因子分解到输入图。

总之，研究人员表明，Spectra在具有挑战性的肿瘤免疫环境中优于现有方法，因为它发现了在免疫检查点治疗下发生变化的因素，解开了CD8⁺ T细胞肿瘤反应性和衰竭的高度相关特征，找到了一个解释治疗下巨噬细胞状态持续变化的程序，并确定了细胞类型特异性免疫代谢程序。

附：英文原文

Title: Supervised discovery of interpretable gene programs from single-cell data

Author: Kunes, Russell Z., Walle, Thomas, Land, Max, Nawy, Tal, Peer, Dana

Issue&Volume: 2023-09-21

Abstract: Factor analysis decomposes single-cell gene expression data into a minimal set of gene programs that correspond to processes executed by cells in a sample. However, matrix factorization methods are prone to technical artifacts and poor factor interpretability. We address these concerns with Spectra, an algorithm that combines user-provided gene programs with the detection of novel programs that together best explain expression covariation. Spectra incorporates existing gene sets and cell-type labels as prior biological information, explicitly models cell type and represents input gene sets as a gene–gene knowledge graph using a penalty function to guide factorization toward the input graph. We show that Spectra outperforms existing approaches in challenging tumor immune contexts, as it finds factors that change under immune checkpoint therapy, disentangles the highly correlated features of CD8+ T cell tumor reactivity and exhaustion, finds a program that explains continuous macrophage state changes under therapy and identifies cell-type-specific immune metabolic programs.

DOI: 10.1038/s41587-023-01940-3

Source: https://www.nature.com/articles/s41587-023-01940-3