Inference of single-cell network using mutual information for scRNA-seq data analysis.

Autor: Chang LY; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan., Hao TY; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan., Wang WJ; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan., Lin CY; Institute of Bioinformatics and Systems Biology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan. chunyulin@nycu.edu.tw.; Department of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan. chunyulin@nycu.edu.tw.; Institute of Data Science and Engineering, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan. chunyulin@nycu.edu.tw.; Center for Intelligent Drug Systems and Smart Bio-Devices, National Yang Ming Chiao Tung University, Hsinchu, 300, Taiwan. chunyulin@nycu.edu.tw.; Cancer and Immunology Research Center, National Yang Ming Chiao Tung University, Taipei, 112, Taiwan. chunyulin@nycu.edu.tw.; School of Dentistry, Kaohsiung Medical University, Kaohsiung, 807, Taiwan. chunyulin@nycu.edu.tw.
Jazyk: angličtina
Zdroj: BMC bioinformatics [BMC Bioinformatics] 2024 Sep 05; Vol. 25 (Suppl 2), pp. 292. Date of Electronic Publication: 2024 Sep 05.
DOI: 10.1186/s12859-024-05895-3
Abstrakt: Background: With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.
Results: We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.
Conclusions: SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .
(© 2024. The Author(s).)
Databáze: MEDLINE
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