Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
文献类型：期刊论文
| 作者 | Zhang, Lihua1,2; Zhang, Shihua1,2,3
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| 刊名 | IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
 |
| 出版日期 | 2020-03-01 |
| 卷号 | 17期号:2页码:376-389 |
| 关键词 | Gene expression Sequential analysis Bioinformatics RNA Matrix converters Computational biology Bayes methods Single-cell RNA-sequencing technique dropout event imputation algorithm bioinformatics |
| ISSN号 | 1545-5963 |
| DOI | 10.1109/TCBB.2018.2848633 |
| 英文摘要 | Single-cell RNA-sequencing (scRNA-seq) is a recent breakthrough technology, which paves the way for measuring RNA levels at single cell resolution to study precise biological functions. One of the main challenges when analyzing scRNA-seq data is the presence of zeros or dropout events, which may mislead downstream analyses. To compensate the dropout effect, several methods have been developed to impute gene expression since the first Bayesian-based method being proposed in 2016. However, these methods have shown very diverse characteristics in terms of model hypothesis and imputation performance. Thus, large-scale comparison and evaluation of these methods is urgently needed now. To this end, we compared eight imputation methods, evaluated their power in recovering original real data, and performed broad analyses to explore their effects on clustering cell types, detecting differentially expressed genes, and reconstructing lineage trajectories in the context of both simulated and real data. Simulated datasets and case studies highlight that there are no one method performs the best in all the situations. Some defects of these methods such as scalability, robustness, and unavailability in some situations need to be addressed in future studies. |
| 资助项目 | National Natural Science Foundation of China[11661141019] ; National Natural Science Foundation of China[61621003] ; National Natural Science Foundation of China[61422309] ; National Natural Science Foundation of China[61379092] ; Strategic Priority Research Program of the Chinese Academy of Sciences (CAS)[XDB13040600] ; National Ten Thousand Talent Program for Young Top-notch Talents ; Key Research Program of the Chinese Academy of Sciences[KFZD-SW-219] ; CAS Frontier Science Research Key Project for Top Young Scientist[QYZDB-SSW-SYS008] |
| WOS研究方向 | Biochemistry & Molecular Biology ; Computer Science ; Mathematics |
| 语种 | 英语 |
| WOS记录号 | WOS:000524236800002 |
| 出版者 | IEEE COMPUTER SOC |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/51157]  |
| 专题 | 应用数学研究所 |
| 通讯作者 | Zhang, Shihua |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Math Sci, Beijing 100049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, RCSDS, NCMIS,CEMS, Beijing 100190, Peoples R China 3.Chinese Acad Sci, Ctr Excellence Anim Evolut & Genet, Kunming 650223, Yunnan, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Lihua,Zhang, Shihua. Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data[J]. IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,2020,17(2):376-389. |
| APA | Zhang, Lihua,&Zhang, Shihua.(2020).Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data.IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS,17(2),376-389. |
| MLA | Zhang, Lihua,et al."Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data".IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 17.2(2020):376-389. |
入库方式： OAI收割
来源：数学与系统科学研究院
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