用于揭示细胞异质性的单细胞染色质可及性测序数据的离散潜在嵌入。

IF 12 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang
{"title":"用于揭示细胞异质性的单细胞染色质可及性测序数据的离散潜在嵌入。","authors":"Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang","doi":"10.1038/s43588-024-00625-4","DOIUrl":null,"url":null,"abstract":"Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models—especially variational autoencoders—have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE’s capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively. A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.","PeriodicalId":74246,"journal":{"name":"Nature computational science","volume":"4 5","pages":"346-359"},"PeriodicalIF":12.0000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity\",\"authors\":\"Xuejian Cui, Xiaoyang Chen, Zhen Li, Zijing Gao, Shengquan Chen, Rui Jiang\",\"doi\":\"10.1038/s43588-024-00625-4\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models—especially variational autoencoders—have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE’s capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively. A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.\",\"PeriodicalId\":74246,\"journal\":{\"name\":\"Nature computational science\",\"volume\":\"4 5\",\"pages\":\"346-359\"},\"PeriodicalIF\":12.0000,\"publicationDate\":\"2024-05-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Nature computational science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.nature.com/articles/s43588-024-00625-4\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Nature computational science","FirstCategoryId":"1085","ListUrlMain":"https://www.nature.com/articles/s43588-024-00625-4","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0

摘要

单细胞表观基因组数据正以前所未有的速度持续增长,但其高维性和稀疏性等特点给下游分析带来了巨大挑战。虽然深度学习模型--尤其是变异自动编码器--已被广泛用于捕捉低维特征嵌入,但流行的高斯假设与实际数据有些不符,而且这些模型往往难以纳入来自丰富细胞图谱的参考信息。在这里,我们提出了 CASTLE,一种基于向量量化变异自动编码器框架的深度生成模型,用于提取离散的潜在嵌入,以解释单细胞染色质可及性测序数据的特征。与最先进的方法相比,我们验证了 CASTLE 在准确识别细胞类型和合理可视化方面的性能和稳健性。我们证明了 CASTLE 以弱监督或监督方式有效整合现有海量参考数据集的优势。我们进一步证明了 CASTLE 能够直观地提炼出细胞类型特异性特征谱,从而定量地揭示细胞的异质性和生物学意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity

Discrete latent embedding of single-cell chromatin accessibility sequencing data for uncovering cell heterogeneity
Single-cell epigenomic data has been growing continuously at an unprecedented pace, but their characteristics such as high dimensionality and sparsity pose substantial challenges to downstream analysis. Although deep learning models—especially variational autoencoders—have been widely used to capture low-dimensional feature embeddings, the prevalent Gaussian assumption somewhat disagrees with real data, and these models tend to struggle to incorporate reference information from abundant cell atlases. Here we propose CASTLE, a deep generative model based on the vector-quantized variational autoencoder framework to extract discrete latent embeddings that interpretably characterize single-cell chromatin accessibility sequencing data. We validate the performance and robustness of CASTLE for accurate cell-type identification and reasonable visualization compared with state-of-the-art methods. We demonstrate the advantages of CASTLE for effective incorporation of existing massive reference datasets in a weakly supervised or supervised manner. We further demonstrate CASTLE’s capacity for intuitively distilling cell-type-specific feature spectra that unveil cell heterogeneity and biological implications quantitatively. A method based on a vector-quantized variational autoencoder, called CASTLE, can interpretably extract discrete latent embeddings and quantitatively generate the cell-type-specific feature spectrum for single-cell chromatin accessibility sequencing data.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
11.70
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信