Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types
文献类型：期刊论文
| 作者 | Wu, Wenming5; Zhang, Wensheng2,3,4 ; Ma, Xiaoke1,5
|
| 刊名 | BRIEFINGS IN BIOINFORMATICS
 |
| 出版日期 | 2022-03-10 |
| 卷号 | 23期号:2页码:13 |
| ISSN号 | 1467-5463 |
| 关键词 | single-cell multi-omics data integrative analysis adaptive graph learning cell type |
| DOI | 10.1093/bib/bbab546 |
| 通讯作者 | Ma, Xiaoke(xkma@xidian.edu.cn) |
| 英文摘要 | Advances in single-cell biotechnologies simultaneously generate the transcriptomic and epigenomic profiles at cell levels, providing an opportunity for investigating cell fates. Although great efforts have been devoted to either of them, the integrative analysis of single-cell multi-omics data is really limited because of the heterogeneity, noises and sparsity of single-cell profiles. In this study, a network-based integrative clustering algorithm (aka NIC) is present for the identification of cell types by fusing the parallel single-cell transcriptomic (scRNA-seq) and epigenomic profiles (scATAC-seq or DNA methylation). To avoid heterogeneity of multi-omics data, NIC automatically learns the cell-cell similarity graphs, which transforms the fusion of multi-omics data into the analysis of multiple networks. Then, NIC employs joint non-negative matrix factorization to learn the shared features of cells by exploiting the structure of learned cell-cell similarity networks, providing a better way to characterize the features of cells. The graph learning and integrative analysis procedures are jointly formulated as an optimization problem, and then the update rules are derived. Thirteen single-cell multi-omics datasets from various tissues and organisms are adopted to validate the performance of NIC, and the experimental results demonstrate that the proposed algorithm significantly outperforms the state-of-the-art methods in terms of various measurements. The proposed algorithm provides an effective strategy for the integrative analysis of single-cell multi-omics data (The software is coded using Matlab, and is freely available for academic ). |
| WOS关键词 | HETEROGENEITY ; DYNAMICS |
| 资助项目 | National Key Research and Development Program of China[2018AAA0102100] ; National Natural Science Foundation of China[61961160707] ; National Natural Science Foundation of China[61976212] ; National Natural Science Foundation of China[61772394] ; Key Research and Development Program of Shaanxi[2021ZDLGY02-02] ; Key Research and Development Program of Gansu[21YF5GA063] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Mathematical & Computational Biology |
| 语种 | 英语 |
| 出版者 | OXFORD UNIV PRESS |
| WOS记录号 | WOS:000804196500065 |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Key Research and Development Program of Shaanxi ; Key Research and Development Program of Gansu |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49635]  |
| 专题 | 精密感知与控制研究中心_人工智能与机器学习 |
| 通讯作者 | Ma, Xiaoke |
| 作者单位 | 1.Univ Iowa, Iowa City, IA 52242 USA 2.Univ Chinese Acad Sci, Beijing, Peoples R China 3.Chinese Acad Sci, Inst Automat, Res & Dev Dept, Beijing, Peoples R China 4.Chinese Acad Sci, Inst Automat, Machine Learning & Data Min, Beijing, Peoples R China 5.Xidian Univ, Sch Comp Sci & Technol, Xian, Shaanxi, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wu, Wenming,Zhang, Wensheng,Ma, Xiaoke. Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types[J]. BRIEFINGS IN BIOINFORMATICS,2022,23(2):13. |
| APA | Wu, Wenming,Zhang, Wensheng,&Ma, Xiaoke.(2022).Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types.BRIEFINGS IN BIOINFORMATICS,23(2),13. |
| MLA | Wu, Wenming,et al."Network-based integrative analysis of single-cell transcriptomic and epigenomic data for cell types".BRIEFINGS IN BIOINFORMATICS 23.2(2022):13. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。