ArticlePLoS computational biology2024
Quantifying the clusterness and trajectoriness of single-cell RNA-seq data.
Article in PLoS computational biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- On feature selection to disentangle cell type and state transcriptional programs.BMC genomics · 2025Article
- Trajectory inference from single-cell genomics data with a process time model.PLoS computational biology · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- scCCTR: An iterative selection-based semi-supervised clustering model for single-cell RNA-seq data.Computational and structural biotechnology journal · 2025Article
- Biofunctionalized gelatin hydrogels support development and maturation of iPSC-derived cortical organoids.Cell reports · 2024Article
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2 authors.
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Abstract
Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.
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