scMFG: a single-cell multi-omics integration method based on feature grouping

• Published in: BMC genomics Vol. 26; no. 1; pp. 132 - 13
• Main Authors: Ma, Litian, Liu, Jingtao, Sun, Wei, Zhao, Chenguang, Yu, Liang
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 11.02.2025; BioMed Central; BMC
• Subjects: Algorithms; Animals; Annotations; Background noise; Biological analysis; Biological research; Biology, Experimental; Cells; Computational Biology - methods; Datasets; DNA methylation; Electronic data processing; Feature grouping; Feature selection; Gene expression; Genetic aspects; Genomics; Genomics - methods; Heterogeneity; Humans; Identification and classification; Integration; Kidneys; Methods; Mice; Molecular biology; Multi-omics; Multiomics; Neonates; Neural networks; Performance evaluation; Quality control; Robustness; Single-cell; Single-Cell Analysis - methods; China; Integration; Multi-omics; Single-cell; Feature grouping

Recent advancements in methodologies and technologies have enabled the simultaneous measurement of multiple omics data, which provides a comprehensive understanding of cellular heterogeneity. However, existing methods have limitations in accurately identifying cell types while maintaining model inte...