ArticleNature communications2024
Statistical method scDEED for detecting dubious 2D single-cell embeddings and optimizing t-SNE and UMAP hyperparameters.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 31 papers.
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31 citing papers in PubMed.
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- Dual-Similarity Driven Just-in-Time Modeling Enables Precise Real-Time Monitoring of Cell Cultures via Dielectric Spectroscopy.Biotechnology journal · 2026Article
- Alk-Fam150b (augmentor α) expression in the paraventricular nucleus of the mouse hypothalamus at molecular resolution, and its sensitivity to acute stress.Journal of neuroendocrinology · 2026Article
- Interactive visualization of metric distortion in nonlinear data embeddings using the distortions package.Briefings in bioinformatics · 2026Article
- Mathematical analysis of G1/S sub-networks in hematopoietic proliferation: Sequential and lineage-specific activation.iScience · 2026Article
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- Mapping cellular interactions and communication landscapes in cervical cancer via single-cell transcriptomics.Discover oncology · 2025Article
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis.Nature communications · 2025Article
- shinyUMAP: an online tool for promoting understanding of single cell omics data visualization.bioRxiv : the preprint server for biology · 2025Article
- Heterogeneity of the liver cancer tumor microenvironment: mitochondrial metabolism and causal inference through Mendelian randomization.Discover oncology · 2025Article
- Quality prediction method for automotive body resistance spot welding based on digital twin technology.Scientific reports · 2025Article
- Paradigms, innovations, and biological applications of RNA velocity: a comprehensive review.Briefings in bioinformatics · 2025Review
- Iterative clustering algorithm G-DESC-E and pan-cancer key gene analysis based on single-cell sequencing data.Briefings in bioinformatics · 2025Article
- Assessing and improving reliability of neighbor embedding methods: a map-continuity perspective.Nature communications · 2025Article
- Identification of predictive subphenotypes for clinical outcomes using real world data and machine learning.Nature communications · 2025Article
- Evaluating discrepancies in dimensionality reduction for time-series single-cell RNA-sequencing data.Briefings in bioinformatics · 2025Article
- Neuronal activity modulates the expression of secretagogin, a CaActa physiologica (Oxford, England) · 2025Article
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Abstract
Two-dimensional (2D) embedding methods are crucial for single-cell data visualization. Popular methods such as t-distributed stochastic neighbor embedding (t-SNE) and uniform manifold approximation and projection (UMAP) are commonly used for visualizing cell clusters; however, it is well known that t-SNE and UMAP's 2D embeddings might not reliably inform the similarities among cell clusters. Motivated by this challenge, we present a statistical method, scDEED, for detecting dubious cell embeddings output by a 2D-embedding method. By calculating a reliability score for every cell embedding based on the similarity between the cell's 2D-embedding neighbors and pre-embedding neighbors, scDEED identifies the cell embeddings with low reliability scores as dubious and those with high reliability scores as trustworthy. Moreover, by minimizing the number of dubious cell embeddings, scDEED provides intuitive guidance for optimizing the hyperparameters of an embedding method. We show the effectiveness of scDEED on multiple datasets for detecting dubious cell embeddings and optimizing the hyperparameters of t-SNE and UMAP.
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