In Genomics, proteomics & bioinformatics
Recently developed technologies to generate single-cell genomic data have made a revolutionary impact in the field of biology. Multi-omics assays offer even greater opportunities to understand cellular states and biological processes. The problem of integrating different omics data with very different dimensionality and statistical properties remains, however, quite challenging. A growing body of computational tools is being developed for this task, leveraging ideas ranging from machine translation to the theory of networks, and represents another frontier on the interface of biology and data science. Our goal in this review paper is to provide a comprehensive, up-to-date survey of computational techniques for the integration of single-cell multi-omics data while making the concepts behind each algorithm approachable to a non-expert audience.
Stanojevic Stefan, Li Yijun, Ristivojevic Aleksandar, Garmire Lana X
2022-Dec-26
Integration, Machine learning, Multi-omics, Single-cell, Unsupervised learning
