DELVE: feature selection for preserving biological trajectories in single-cell data

Jolene S. Ranek, Wayne Stallaert, J. Justin Milner, Margaret Redick, Samuel C. Wolff, Adriana S. Beltran, Natalie Stanley () and Jeremy E. Purvis ()
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Jolene S. Ranek: University of North Carolina at Chapel Hill
Wayne Stallaert: University of Pittsburgh
J. Justin Milner: University of North Carolina at Chapel Hill
Margaret Redick: University of North Carolina at Chapel Hill
Samuel C. Wolff: University of North Carolina at Chapel Hill
Adriana S. Beltran: University of North Carolina at Chapel Hill
Natalie Stanley: University of North Carolina at Chapel Hill
Jeremy E. Purvis: University of North Carolina at Chapel Hill

Nature Communications, 2024, vol. 15, issue 1, 1-26

Abstract: Abstract Single-cell technologies can measure the expression of thousands of molecular features in individual cells undergoing dynamic biological processes. While examining cells along a computationally-ordered pseudotime trajectory can reveal how changes in gene or protein expression impact cell fate, identifying such dynamic features is challenging due to the inherent noise in single-cell data. Here, we present DELVE, an unsupervised feature selection method for identifying a representative subset of molecular features which robustly recapitulate cellular trajectories. In contrast to previous work, DELVE uses a bottom-up approach to mitigate the effects of confounding sources of variation, and instead models cell states from dynamic gene or protein modules based on core regulatory complexes. Using simulations, single-cell RNA sequencing, and iterative immunofluorescence imaging data in the context of cell cycle and cellular differentiation, we demonstrate how DELVE selects features that better define cell-types and cell-type transitions. DELVE is available as an open-source python package: https://github.com/jranek/delve .

Date: 2024
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DOI: 10.1038/s41467-024-46773-z

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