ArticleNature computational science2024
Mapping the gene space at single-cell resolution with gene signal pattern analysis.
Article in Nature computational science, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Advancements in CRISPR/Cas Technologies for Sensitive Cancer Detection: Mechanisms, Platforms, and Clinical Translation Roadmap.Diagnostics (Basel, Switzerland) · 2026Review
- Harnessing Deep Learning Models for Guide RNA Optimization and Off-Target Prediction in CRISPR Systems.Biotechnology journal · 2026Review
- Locat: Joint enrichment and depletion testing identifies localized marker genes in single-cell transcriptomics.bioRxiv : the preprint server for biology · 2026Article
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Authors and funding
8 authors.
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Abstract
In single-cell sequencing analysis, several computational methods have been developed to map the cellular state space, but little has been done to map or create embeddings of the gene space. Here we formulate the gene embedding problem, design tasks with simulated single-cell data to evaluate representations, and establish ten relevant baselines. We then present a graph signal processing approach, called gene signal pattern analysis (GSPA), that learns rich gene representations from single-cell data using a dictionary of diffusion wavelets on the cell-cell graph. GSPA enables characterization of genes based on their patterning and localization on the cellular manifold. We motivate and demonstrate the efficacy of GSPA as a framework for diverse biological tasks, such as capturing gene co-expression modules, condition-specific enrichment and perturbation-specific gene-gene interactions. Then we showcase the broad utility of gene representations derived from GSPA, including for cell-cell communication (GSPA-LR), spatial transcriptomics (GSPA-multimodal) and patient response (GSPA-Pt) analysis.
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