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

• Published in: Nature communications Vol. 12; no. 1; pp. 5528 - 18
• Main Authors: Stassen, Shobana V., Yip, Gwinky G. K., Wong, Kenneth K. Y., Ho, Joshua W. K., Tsia, Kevin K.
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 20-09-2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114/2397; 631/114/2415; Algorithms; Animals; Automation; Cell Cycle; Cell Differentiation; Cell Line, Tumor; Cell Shape; Cell size; Cellular structure; Complexity; Datasets; Gene expression; Genomics; Hematopoiesis; Humanities and Social Sciences; Humans; Inference; Islets of Langerhans - cytology; LIM-Homeodomain Proteins - metabolism; Mesoderm - cytology; Mice; Morphology; Mouse Embryonic Stem Cells - cytology; multidisciplinary; Organogenesis; Proteomics; Random walk; Science; Science (multidisciplinary); Single-Cell Analysis; Stem cells; Teleportation; Topology; Transcription Factors - metabolism
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-021-25773-3

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.
Scalable trajectory inference for multi-omic single cell datasets is challenging in terms of capturing non-tree complex topologies. Here the authors present a method, VIA, that scales to millions of cells across multiple omic modalities using lazy-teleporting random walks.