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

Author:

Ranek Jolene S.,Stallaert WayneORCID,Milner J. JustinORCID,Redick MargaretORCID,Wolff Samuel C.,Beltran Adriana S.,Stanley NatalieORCID,Purvis Jeremy E.ORCID

Abstract

AbstractSingle-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.

Funder

U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences

U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute

Publisher

Springer Science and Business Media LLC

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