scTOP: physics-inspired order parameters for cellular identification and visualization

Abstract excerpt

Advances in single-cell RNA-sequencing (scRNA-seq) provide an unprecedented window into cellular identity. The increasing abundance of data requires new theoretical and computational frameworks for understanding cell fate determination, accurately classifying cell fates from expression data, and integrating knowledge from cell atlases. Here, we present single-cell Type Order Parameters (scTOP): a statistical-physi...

Topics

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• Biomarkers
• Cell Cycle
• Cell Differentiation
• Gene Expression Profiling
• Gene Expression Regulation
• Hematopoietic Stem Cells
• Single-Cell Analysis
• Single-cell and spatial transcriptomics

Identifiers and source

Literature Corpus work

77561dd5-0a07-5f6f-899d-7112a08ece0c

DOI

10.1101/2023.01.25.525581