气候变化领域集成服务门户(CCPortal): Integration and transfer learning of single-cell transcriptomes via cFIT

CCPortal

DOI	10.1073/pnas.2024383118
	Integration and transfer learning of single-cell transcriptomes via cFIT
	Peng M.; Li Y.; Wamsley B.; Wei Y.; Roeder K.
发表日期	2021
ISSN	00278424
卷号	118 期号:10
英文摘要	Large, comprehensive collections of single-cell RNA sequencing (scRNA-seq) datasets have been generated that allow for the full transcriptional characterization of cell types across a wide variety of biological and clinical conditions. As new methods arise to measure distinct cellular modalities, a key analytical challenge is to integrate these datasets or transfer knowledge from one to the other to better understand cellular identity and functions. Here, we present a simple yet surprisingly effective method named common factor integration and transfer learning (cFIT) for capturing various batch effects across experiments, technologies, subjects, and even species. The proposed method models the shared information between various datasets by a common factor space while allowing for unique distortions and shifts in genewise expression in each batch. The model parameters are learned under an iterative nonnegative matrix factorization (NMF) framework and then used for synchronized integration from across-domain assays. In addition, the model enables transferring via low-rank matrix from more informative data to allow for precise identification in data of lower quality. Compared with existing approaches, our method imposes weaker assumptions on the cell composition of each individual dataset; however, it is shown to be more reliable in preserving biological variations. We apply cFIT to multiple scRNA-seq datasets of developing brain from human and mouse, varying by technologies and developmental stages. The successful integration and transfer uncover the transcriptional resemblance across systems. The study helps establish a comprehensive landscape of brain cell-type diversity and provides insights into brain development. © 2021 National Academy of Sciences. All rights reserved.
英文关键词	Brain cells; Data integration; Single-cell RNA-seq; Transfer learning
语种	英语
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/180344
作者单位	Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, United States; Neurogenetics Program, University of California, Los Angeles, CA 90095, United States; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, United States
推荐引用方式 GB/T 7714	Peng M.,Li Y.,Wamsley B.,et al. Integration and transfer learning of single-cell transcriptomes via cFIT[J],2021,118(10).
APA	Peng M.,Li Y.,Wamsley B.,Wei Y.,&Roeder K..(2021).Integration and transfer learning of single-cell transcriptomes via cFIT.Proceedings of the National Academy of Sciences of the United States of America,118(10).
MLA	Peng M.,et al."Integration and transfer learning of single-cell transcriptomes via cFIT".Proceedings of the National Academy of Sciences of the United States of America 118.10(2021).