Postprint online available 07/2019 Open Access Green as soon as is submitted to ZB.
Statistical single cell multi-omics integration.
Curr. Opin. Syst. Biol. 7, 54-59 (2018)
Single cell high throughput genomic measurements are revolutionizing the fields of biology and medicine, providing a means to tackle biological problems that have thus far been inaccessible, such as the systematic discovery of new cell types, the identification of cellular heterogeneity in health and disease, or the cell-fate decisions taking place during differentiation and reprogramming. Recently implemented multi–omics measurements of genomes, transcriptomes, epigenomes, proteomes and chromatin organization are opening up new avenues to begin to disentangle the causal relationship between -omics layers and how these co-determine higher-order cellular phenotypes. This technological revolution is not restricted to basic science but promises major breakthroughs in medical diagnostics and treatments. In this paper we review existing computational methods for the analysis and integration of different -omics layers and discuss what new approaches are needed to leverage the full potential of single cell multi-omics data.
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Publication type Article: Journal article
Document type Review
Institute(s) Institute of Computational Biology (ICB)