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

Shobana V Stassen,Joshua W K Ho,Gwinky G K Yip,Kenneth K Y Wong,Kevin K Tsia

doi:10.1038/s41467-021-25773-3

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.

Highlights

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science
In VIA, we show that for cytometry data there is no need for any dimensionality reduction, and for transcriptomic data we show that VIA does not need a second dimensionality reduction step but robustly infers lineages on a wide range of input principal components (PCs)
With the growing scale and complexity of single-cell datasets, there is an unmet need for accurate cell fate prediction and lineage detection in complex topologies manifested in biology

Summary

Introduction

Inferring cellular trajectories using a variety of omic data is a critical task in single-cell data science. The growing scale of single-cell data, notably cell atlases of whole organisms[4,6], embryos[7,8], and human organs[9], exceeds the existing TI capacity, not just in runtime and memory, but in preserving both the fine-grain resolution of the embedded trajectories and the global connectivity among them Very often, such global information is lost in current TI methods after extensive and multiple rounds of dimension reduction or subsampling. We show in subsequent sections that VIA accurately detects minor dendritic sub-populations and their characteristic gene expression trends in human hematopoiesis; automatically identifies pancreatic islets including rare delta cells; and recovers endothelial and cardiomyocyte bifurcation in integrated data sets of singlecell RNA-sequencing (scRNA-seq) and single-cell sequencing assay for transposase-accessible chromatin (scATAC-seq) Another defining attribute of VIA is its resilience in handling the wide disparity in single-cell data size, structure and dimensionality across modalities. Validated with the in situ fluorescence (FL) image capture, we found that VIA reliably reconstructs the continuous cell-cycle progressions from G1-S-G2/M phase, and reveals subtle changes in cell mass accumulation

Methods

Results

Conclusion