HSUEH-FEN JUANHuang, Hsuan ChengHsuan ChengHuang2023-02-172023-02-172023-01-0117590876https://scholars.lib.ntu.edu.tw/handle/123456789/628341The study of multiple “omes,” such as the genome, transcriptome, proteome, and metabolome has become widespread in biomedical research. High-throughput techniques enable the rapid generation of high-dimensional multiomics data. This multiomics approach provides a more complete perspective to study biological systems compared with traditional methods. However, the quantitative analysis and integration of distinct types of high-dimensional omics data remain a challenge. Here, we provide an up-to-date and comprehensive review of the methods used for omics data quantification and integration. We first review the quantitative analysis of not only bulk but also single-cell transcriptomics data, as well as proteomics data. Current methods for reducing batch effects and integrating heterogeneous high-dimensional data are then introduced. Network analysis on large-scale biomedical data can capture the global properties of drugs, targets, and disease relationships, thus enabling a better understanding of biological systems. Current trends in the applications and methods used to extend quantitative omics data analysis to biological networks are also discussed. This article is categorized under: Data Science > Artificial Intelligence/Machine Learning.enbiological network | data integration | multiomics data | quantitative analysis | single-cell transcriptomics[SDGs]SDG3review10.1002/wcms.16582-s2.0-85147376823https://api.elsevier.com/content/abstract/scopus_id/85147376823