Generalized and scalable trajectory inference in single-cell omics data with VIA

Shobana V. Stassen, Gwinky G. K. Yip, Kenneth K. Y. Wong, Joshua W. K. Ho and Kevin K. Tsia ()
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Shobana V. Stassen: The University of Hong Kong
Gwinky G. K. Yip: The University of Hong Kong
Kenneth K. Y. Wong: The University of Hong Kong
Joshua W. K. Ho: The University of Hong Kong
Kevin K. Tsia: The University of Hong Kong

Nature Communications, 2021, vol. 12, issue 1, 1-18

Abstract: Abstract Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. However, accurate prediction of cell fates, and thereby biologically meaningful discovery, is challenged by the sheer size of single-cell data, the diversity of omic data types, and the complexity of their topologies. We present VIA, a scalable trajectory inference algorithm that overcomes these limitations by using lazy-teleporting random walks to accurately reconstruct complex cellular trajectories beyond tree-like pathways (e.g., cyclic or disconnected structures). We show that VIA robustly and efficiently unravels the fine-grained sub-trajectories in a 1.3-million-cell transcriptomic mouse atlas without losing the global connectivity at such a high cell count. We further apply VIA to discovering elusive lineages and less populous cell fates missed by other methods across a variety of data types, including single-cell proteomic, epigenomic, multi-omics datasets, and a new in-house single-cell morphological dataset.

Date: 2021
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DOI: 10.1038/s41467-021-25773-3

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