气候变化领域集成服务门户(CCPortal): BABEL enables cross-modality translation between multiomic profiles at single-cell resolution

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DOI	10.1073/pnas.2023070118
	BABEL enables cross-modality translation between multiomic profiles at single-cell resolution
	Wu K.E.; Yost K.E.; Chang H.Y.; Zou J.
发表日期	2021
ISSN	00278424
卷号	118 期号:15
英文摘要	Simultaneous profiling of multiomic modalities within a single cell is a grand challenge for single-cell biology. While there have been impressive technical innovations demonstrating feasibility-for example, generating paired measurements of single-cell transcriptome (single-cell RNA sequencing [scRNA-seq]) and chromatin accessibility (single-cell assay for transposase-accessible chromatin using sequencing [scATAC-seq])-widespread application of joint profiling is challenging due to its experimental complexity, noise, and cost. Here, we introduce BABEL, a deep learning method that translates between the transcriptome and chromatin profiles of a single cell. Leveraging an interoperable neural network model, BABEL can predict single-cell expression directly from a cell's scATAC-seq and vice versa after training on relevant data. This makes it possible to computationally synthesize paired multiomic measurements when only one modality is experimentally available. Across several paired single-cell ATAC and gene expression datasets in human and mouse, we validate that BABEL accurately translates between these modalities for individual cells. BABEL also generalizes well to cell types within new biological contexts not seen during training. Starting from scATAC-seq of patient-derived basal cell carcinoma (BCC), BABEL generated single-cell expression that enabled fine-grained classification of complex cell states, despite having never seen BCC data. These predictions are comparable to analyses of experimental BCC scRNA-seq data for diverse cell types related to BABEL's training data. We further show that BABEL can incorporate additional single-cell data modalities, such as protein epitope profiling, thus enabling translation across chromatin, RNA, and protein. BABEL offers a powerful approach for data exploration and hypothesis generation. © This open access article is distributed under Creative Commons Attribution License 4.0 (CC BY).
英文关键词	Deep learning; Gene regulation; Multiomics; Single-cell analysis
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/179937
作者单位	Department of Computer Science, Stanford University, Stanford, CA 94305, United States; Department of Biomedical Data Science, Stanford University, School of Medicine, Stanford, CA 94305, United States; Center for Personal and Dynamic Regulomes, Stanford University, School of Medicine, Stanford, CA 94305, United States; HHMI, Stanford University, School of Medicine, Stanford, CA 94305, United States
推荐引用方式 GB/T 7714	Wu K.E.,Yost K.E.,Chang H.Y.,et al. BABEL enables cross-modality translation between multiomic profiles at single-cell resolution[J],2021,118(15).
APA	Wu K.E.,Yost K.E.,Chang H.Y.,&Zou J..(2021).BABEL enables cross-modality translation between multiomic profiles at single-cell resolution.Proceedings of the National Academy of Sciences of the United States of America,118(15).
MLA	Wu K.E.,et al."BABEL enables cross-modality translation between multiomic profiles at single-cell resolution".Proceedings of the National Academy of Sciences of the United States of America 118.15(2021).