Quantitative analysis of high-throughput biological data

The 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

Automated summary

The study of multiple omes is widely used in biomedical research to gain a comprehensive perspective on biological systems. The generation of high-dimensional multiomics data through high-throughput techniques is enabled, but the quantitative analysis and integration of different types of omics data pose challenges. This article provides an up-to-date review on the methods used for quantification and integration of omics data, focusing on transcriptomics, proteomics, and batch effects reduction. The potential of network analysis in understanding biological systems and the current trends in extending quantitative omics data analysis to biological networks are also discussed.