英文论文


文献类型
Article
题名
CITEMOXMBD: A flexible single-cell multimodal omics analysis framework to reveal the heterogeneity of immune cells
作者
Hu, Huan; Liu, Ruiqi; Zhao, Chunlin; Lu, Yuer; Xiong, Yichun; Chen, Lingling; Jin, Jun; Ma, Yunlong; Su, Jianzhong; Yu, Zhengquan; Cheng, Feng; Ye, Fangfu; Liu, Liyu; Zhao, Qi; Shuai, Jianwei
作者单位
[Hu, Huan; Lu, Yuer; Chen, Lingling; Jin, Jun; Cheng, Feng; Shuai, Jianwei] Xiamen Univ, Dept Phys, Xiamen, Peoples R China. [Hu, Huan; Lu, Yuer; Chen, Lingling; Jin, Jun; Cheng, Feng; Shuai, Jianwei] Xiamen Univ, Fujian Prov Key Lab Soft Funct Mat Res, Xiamen 361005, Peoples R China. [Hu, Huan; Jin, Jun; Shuai, Jianwei] Xiamen Univ, Innovat Ctr Cell Signaling Network, Natl Inst Data Sci Hlth & Med, Xiamen, Peoples R China. [Hu, Huan; Jin, Jun; Shuai, Jianwei] Xiamen Univ, Innovat Ctr Cell Signaling Network, State Key Lab Cellular Stress Biol, Xiamen, Peoples R China. [Hu, Huan; Su, Jianzhong; Ye, Fangfu; Liu, Liyu; Shuai, Jianwei] Univ Chinese Acad Sci, Wenzhou Inst, Wenzhou, Zhejiang, Peoples R China. [Hu, Huan; Su, Jianzhong; Ye, Fangfu; Liu, Liyu; Shuai, Jianwei] Zhejiang Lab Regenerat Med Vis & Brain Hlth, Oujiang Lab, Wenzhou, Zhejiang, Peoples R China. [Liu, Ruiqi; Yu, Zhengquan] China Agr Univ, Coll Biol Sci, Dept Nutr & Hlth, State Key Labs Agrobiotechnol, Beijing, Peoples R China. [Zhao, Chunlin] Xiamen Univ, Sch Life Sci, Xiamen, Peoples R China. [Xiong, Yichun; Ma, Yunlong] Wenzhou Med Univ, Sch Ophthalmol, Inst Biomed Big Data, Wenzhou, Peoples R China. [Xiong, Yichun; Ma, Yunlong] Wenzhou Med Univ, Optometry & Eye Hosp, Sch Biomed Engn, Wenzhou, Peoples R China. [Ye, Fangfu] Chinese Acad Sci, Inst Phys, Beijing Natl Lab Condensed Matter Phys, Beijing, Peoples R China. [Ye, Fangfu] Chinese Acad Sci, Inst Phys, Lab Soft Matter & Biol Phys, Beijing, Peoples R China. [Ye, Fangfu] Univ Chinese Acad Sci, Sch Phys Sci, Beijing, Peoples R China. [Liu, Liyu] Chongqing Univ, Coll Phys, Chongqing Key Lab Soft Condensed Matter Phys & Sm, Chongqing, Peoples R China. [Zhao, Qi] Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan, Peoples R China.
通讯作者地址
Xiamen Univ, Fujian Prov Key Lab Soft Funct Mat Res, Xiamen 361005, Peoples R China.; Univ Sci & Technol Liaoning, Sch Comp Sci & Software Engn, Anshan, Peoples R China.
Email
zhaoqi@lnu.edu.cn; jianweishuai@xmu.edu.cn
ResearchID
ORCID
期刊名称
RNA BIOLOGY
出版社
TAYLOR & FRANCIS INC
ISSN
1547-6286
出版信息
2022-12-31, 19 (1):290-304.
JCR
影响因子
ISBN
基金
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [11874310, 12090052]; Foundation of Education Department of Liaoning Province [LJKZ0280]
会议名称
会议地点
会议开始日期
会议结束日期
关键词
CITE-seq; multi-omics; data integration; single-cell; immune system
摘要
Simultaneous measurement of multiple modalities in single-cell analysis, represented by CITE-seq, is a promising approach to link transcriptional changes to cellular phenotype and function, requiring new computational methods to define cellular subtypes and states based on multiple data types. Here, we design a flexible single-cell multimodal analysis framework, called CITEMO, to integrate the transcriptome and antibody-derived tags (ADT) data to capture cell heterogeneity from the multi omics perspective. CITEMO uses Principal Component Analysis (PCA) to obtain a low-dimensional representation of the transcriptome and ADT, respectively, and then employs PCA again to integrate these low-dimensional multimodal data for downstream analysis. To investigate the effectiveness of the CITEMO framework, we apply CITEMO to analyse the cell subtypes of Cord Blood Mononuclear Cells (CBMC) samples. Results show that the CITEMO framework can comprehensively analyse single-cell multimodal samples and accurately identify cell subtypes. Besides, we find some specific immune cells that co-express multiple ADT markers. To better describe the co-expression phenomenon, we introduce the co-expression entropy to measure the heterogeneous distribution of the ADT combinations. To further validate the robustness of the CITEMO framework, we analyse Human Bone Marrow Cell (HBMC) samples and identify different states of the same cell type. CITEMO has an excellent performance in identifying cell subtypes and states for multimodal omics data. We suggest that the flexible design idea of CITEMO can be an inspiration for other single-cell multimodal tasks. The complete source code and dataset of the CITEMO framework can be obtained from https://github.com/studentiz/CITEMO.
一级学科
Biochemistry & Molecular Biology
WOS入藏号
WOS:000752077400001
EI收录号
DOI
10.1080/15476286.2022.2027151
ESI
收录于
SCIE

返回