Feature selection for preserving biological trajectories in single-cell data.

ABSTRACT: Single-cell technologies can readily measure the expression of thousands of molecular features from individual cells undergoing dynamic biological processes, such as cellular differentiation, immune response, and disease progression. While examining cells along a computationally ordered pseudotime offers the potential to study how subtle changes in gene or protein expression impact cell fate decision-making, identifying characteristic features that drive continuous biological processes remains difficult to detect from unenriched and noisy single-cell data. Given that all profiled sources of feature variation contribute to the cell-to-cell distances that define an inferred cellular trajectory, including confounding sources of biological variation (e.g. cell cycle or metabolic state) or nois

SUBMITTER: Ranek JS 

PROVIDER: S-EPMC10197710 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature