Intricacies of single-cell multi-omics data integration

Pia Rautenstrauch; Anna Hendrika Cornelia Vlot; Sepideh Saran; Uwe Ohler

doi:10.1016/j.tig.2021.08.012

Intricacies of single-cell multi-omics data integration

Pia Rautenstrauch, Anna Hendrika Cornelia Vlot, Sepideh Saran, Uwe Ohler

Computational and Predictive Biology

Research output: Contribution to journal › Review article › peer-review

29 Scopus citations

Abstract

A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.

Original language	English
Pages (from-to)	128-139
Number of pages	12
Journal	Trends in Genetics
Volume	38
Issue number	2
DOIs	https://doi.org/10.1016/j.tig.2021.08.012
State	Published - Feb 2022

Funding

A.H.C.V., S.S., and U.O. acknowledge support by the Helmholtz-Einstein International Berlin School in Data Science (HEIBRiDS). S.S. and U.O. were partially supported by BMBF grant 01DQ19008 . P.R. and U.O. acknowledge support by DFG Research Unit FOR 2841 and the DFG International Research Training Group IRTG 2403 .

Keywords

cell type identity
method development
multi-omics data integration
multimodal data integration
single-cell multi-omic assays
single-cell omics

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10.1016/j.tig.2021.08.012

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title = "Intricacies of single-cell multi-omics data integration",

abstract = "A wealth of single-cell protocols makes it possible to characterize different molecular layers at unprecedented resolution. Integrating the resulting multimodal single-cell data to find cell-to-cell correspondences remains a challenge. We argue that data integration needs to happen at a meaningful biological level of abstraction and that it is necessary to consider the inherent discrepancies between modalities to strike a balance between biological discovery and noise removal. A survey of current methods reveals that a distinction between technical and biological origins of presumed unwanted variation between datasets is not yet commonly considered. The increasing availability of paired multimodal data will aid the development of improved methods by providing a ground truth on cell-to-cell matches.",

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Intricacies of single-cell multi-omics data integration

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