因子分析将单细胞基因表达数据分解成与样本中细胞执行的过程相对应的基因程序的最小集合。然而，矩阵因子分解方法容易受到技术人为因素的影响，并且因子的解释性较差。我们通过Spectra算法解决了这些问题，该算法将用户提供的基因程序与检测新的程序相结合，共同最佳解释表达协方差。Spectra结合了现有基因集和细胞类型标签作为先前的生物信息，明确建模细胞类型，并将输入的基因集表示为基因-基因知识图，使用惩罚函数来引导因子分解朝着输入图进行。我们证明Spectra在具有挑战性的肿瘤免疫环境中表现优于现有方法，因为它找到了在免疫检查点治疗下发生改变的因子，解开了CD8+ T细胞肿瘤反应性和疲劳的高度相关特征，找到了一个解释在治疗下发生持续巨噬细胞状态变化的程序，并识别了细胞特异性的免疫代谢程序。©2023年。作者（们）。

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.© 2023. The Author(s).