Feng Shuo, Huang Liangfeng, Pournara Anna Vathrakokoili, Huang Ziliang, Yang Xinlu, Zhang Yongjian, Brazma Alvis, Shi Ming, Papatheodorou Irene, Miao Zhichao
GMU-GIBH Joint School of Life Sciences, The Guangdong-Hong Kong-Macao Joint Laboratory for Cell Fate Regulation and Diseases, Guangzhou Laboratory, Guangzhou Medical University, Guangzhou, China.
Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230027, China.
Nat Commun. 2024 Dec 30;15(1):10867. doi: 10.1038/s41467-024-55213-x.
Cell type deconvolution methods can impute cell proportions from bulk transcriptomics data, revealing changes in disease progression or organ development. But benchmarking studies often use simulated bulk data from the same source as the reference, which limits its application scenarios. This study examines batch effects in deconvolution and introduces SCCAF-D, a computational workflow that ensures a Pearson Correlation Coefficient above 0.75 across simulated and real bulk data for various tissue types. Applied to non-alcoholic fatty liver disease, SCCAF-D unveils meaningful insights into changes in cell proportions during disease progression.
细胞类型反卷积方法可以从批量转录组学数据中估算细胞比例，揭示疾病进展或器官发育中的变化。但基准研究通常使用与参考数据来自同一来源的模拟批量数据，这限制了其应用场景。本研究考察了反卷积中的批次效应，并引入了SCCAF-D，这是一种计算工作流程，可确保在各种组织类型的模拟和真实批量数据中，皮尔逊相关系数高于0.75。应用于非酒精性脂肪性肝病时，SCCAF-D揭示了疾病进展过程中细胞比例变化的有意义见解。
