Topology identifies concurrent cyclic processes in single-cell transcriptomics and androgen receptor function

Abstract excerpt

Standard single-cell RNA-seq analysis frameworks aggregate over-lapping biological processes and impose a single parametrization, conflating distinct programs. Here, we introduce a topological framework that detects and disentangles multiple cyclic processes directly from single-cell transcriptomic data. We validate this approach on synthetic datasets and scRNA-seq profiles of human dermal fibroblasts under contro...

Topics

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• Cellular Senescence
• Computational Biology
• Gene Expression Profiling
• Gene Regulatory Networks
• Genetic Markers
• Mice
• Sequence Analysis, RNA
• Single-Cell Gene Expression Analysis

Identifiers and source

Literature Corpus work

ccefe757-1392-5c01-a56c-8caa68b8c146

DOI

10.1101/2025.01.09.632214