ShanghaiTech University Knowledge Management System
| High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis | |
| 2019-04-26 | |
| Source Publication | FRONTIERS IN GENETICS
 |
| ISSN | 1664-8021 |
| Volume | 10 |
| Status | 已发表 |
| DOI | 10.3389/fgene.2019.00371 |
| Abstract | Quantifying or labeling the sample type with high quality is a challenging task, which is a key step for understanding complex diseases. Reducing noise pollution to data and ensuring the extracted intrinsic patterns in concordance with the primary data structure are important in sample clustering and classification. Here we propose an effective data integration framework named as HCI (High-order Correlation Integration), which takes an advantage of high-order correlation matrix incorporated with pattern fusion analysis (PFA), to realize high-dimensional data feature extraction. On the one hand, the high-order Pearson's correlation coefficient can highlight the latent patterns underlying noisy input datasets and thus improve the accuracy and robustness of the algorithms currently available for sample clustering. On the other hand, the PFA can identify intrinsic sample patterns efficiently from different input matrices by optimally adjusting the signal effects. To validate the effectiveness of our new method, we firstly applied HCI on four single-cell RNA-seq datasets to distinguish the cell types, and we found that HCI is capable of identifying the prior-known cell types of single-cell samples from scRNA-seq data with higher accuracy and robustness than other methods under different conditions. Secondly, we also integrated heterogonous omics data from TCGA datasets and GEO datasets including bulk RNA-seq data, which outperformed the other methods at identifying distinct cancer subtypes. Within an additional case study, we also constructed the mRNA-miRNA regulatory network of colorectal cancer based on the feature weight estimated from HCI, where the differentially expressed mRNAs and miRNAs were significantly enriched in well-known functional sets of colorectal cancer, such as KEGG pathways and IPA disease annotations. All these results supported that HCI has extensive flexibility and applicability on sample clustering with different types and organizations of RNA-seq data. |
| Keyword | high-order integration clustering single-cell bulk data analysis |
| Indexed By | SCI ; SCIE |
| Language | 英语 |
| Funding Project | Natural Science Foundation of Shanghai[17ZR1446100] |
| WOS Research Area | Genetics & Heredity |
| WOS Subject | Genetics & Heredity |
| WOS ID | WOS:000466207300001 |
| Publisher | FRONTIERS MEDIA SA |
| WOS Keyword | GENE-EXPRESSION ; SIGNALING PATHWAYS ; DISCOVERY ; MODULES ; HETEROGENEITY ; EMBRYOS ; COLON ; MAPK |
| Original Document Type | Article |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/37520 |
| Collection | 生命科学与技术学院_特聘教授组_陈洛南组 |
| Corresponding Author | Zeng, Tao; Chen, Luonan |
| Affiliation | 1.Univ Chinese Acad Sci, CAS Ctr Excellence Mol Cell Sci, Inst Biochem & Cell Biol, Shanghai Inst Biol Sci,Key Lab Syst Biol,Chinese, Shanghai, Peoples R China 2.Chinese Acad Sci, CAS Ctr Excellence Anim Evolut & Genet, Kunming, Yunnan, Peoples R China 3.ShanghaiTech Univ, Sch Life Sci & Technol, Shanghai, Peoples R China 4.Shanghai Res Ctr Brain Sci & Brain Inspired Intel, Shanghai, Peoples R China |
| Corresponding Author Affilication | School of Life Science and Technology |
| Recommended Citation GB/T 7714 | Tang, Hui,Zeng, Tao,Chen, Luonan. High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis[J]. FRONTIERS IN GENETICS,2019,10. |
| APA | Tang, Hui,Zeng, Tao,&Chen, Luonan.(2019).High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis.FRONTIERS IN GENETICS,10. |
| MLA | Tang, Hui,et al."High-Order Correlation Integration for Single-Cell or Bulk RNA-seq Data Analysis".FRONTIERS IN GENETICS 10(2019). |
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