Unjitwattana Thatchayut, Huang Qianhui, Yang Yiwen, Tao Leyang, Yang Youqi, Zhou Mengtian, Du Yuheng, Garmire Lana X
Department of Biomedical Engineering, University of Michigan, 2200 , Bonisteel, Ann Arbor, MI, 48109, USA.
Department of Computation Medicine and Bioinformatics, University of Michigan, 100 Washtenaw Ave, Ann Arbor, MI, 48109, USA.
Genome Biol. 2025 Mar 11;26(1):52. doi: 10.1186/s13059-025-03495-9.
Single-cell RNA sequencing (scRNA-seq) data from complex human tissues have prevalent blood cell contamination during the sample preparation process. They may also comprise cells of different genetic makeups. We propose a new computational framework, Originator, which deciphers single cells by genetic origin and separates immune cells of blood contamination from those of expected tissue-resident cells. We demonstrate the accuracy of Originator at separating immune cells from the blood and tissue as well as cells of different genetic origins, using a variety of artificially mixed and real datasets, including pancreatic cancer and placentas as examples.
来自复杂人体组织的单细胞RNA测序(scRNA-seq)数据在样本制备过程中普遍存在血细胞污染。它们还可能包含不同基因组成的细胞。我们提出了一种新的计算框架Originator,它可以根据基因来源来解析单个细胞,并将血液污染的免疫细胞与预期的组织驻留细胞中的免疫细胞区分开来。我们以多种人工混合和真实数据集(包括胰腺癌和胎盘为例)证明了Originator在从血液和组织中分离免疫细胞以及不同基因来源细胞方面的准确性。