Originator: Computational Framework Separating Single-Cell RNA-Seq by Genetic and Contextual Information

Thatchayut Unjitwattana; Qianhui Huang; Yuheng Du; Youqi Yang; Mengtian Zhou; Lana X Garmire

doi:10.1101/2024.04.04.588144

Originator: Computational Framework Separating Single-Cell RNA-Seq by Genetic and Contextual Information

bioRxiv [Preprint]. 2024 Apr 6:2024.04.04.588144. doi: 10.1101/2024.04.04.588144.

Authors

Thatchayut Unjitwattana¹, Qianhui Huang², Yuheng Du², Youqi Yang³, Mengtian Zhou⁴, Lana X Garmire²

Affiliations

¹ Department of Biomedical Engineering, University of Michigan, Ann Arbor, MI, USA.
² Department of Computation Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA.
³ Department of Biostatistics, University of Michigan, Ann Arbor, MI, USA.
⁴ Department of Statistics, University of Michigan, Ann Arbor, MI, USA.

Abstract

Single-cell RNA sequencing data from complex human tissues are prone to blood contamination in sample preparation, and some comprise cells of different genetic makeups, necessitating rigorous preprocessing and cell filtering prior to the downstream functional analysis. Our proposed new computational framework, Originator, deciphers single cells by genetic origin and separates blood cells from tissue-resident cells. It improves the quality of data analysis, exemplified by pancreatic ductal adenocarcinoma and placenta tissues.

Keywords: blood cell; cancer; genetic origin; placenta; scRNA-Seq; tissue heterogeneity.

Publication types

Preprint

Abstract

Publication types

Grants and funding