Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data

Hui Ma; Kai Chen

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Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data

Hui Ma, Kai Chen

Genomics · Submitted 1 Oct 2024

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Through various examples, we showcase the practical value of Ut-SNE and underscore the significance of incorporating uncertainty awareness into data visualization practices.

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What the abstract itself states, as probabilities: claims first 0.06 · states limitation 0.16 · human participants 0.11 · controlled comparison 0.08 · randomized assignment 0.01 · states quantitative result 0.04 · compares with prior results 0.23 · mentions code or data availability 0.05.

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Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately. However, t-SNE often fails to account for uncertainties in the original dataset, leading to misleading visualizations where cell subsets with noise appear indistinguishable. To address these challenges, we introduce uncertainty-aware t-SNE (Ut-SNE), a noise-defending visualization tool tailored for uncertain single-cell RNA-seq data. By creating a probabilistic representation for each sample, Our Ut-SNE accurately incorporates noise about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data, revealing significant uncertainties in transcriptomic variability. Through various examples, we showcase the practical value of Ut-SNE and underscore the significance of incorporating uncertainty awareness into data visualization practices. This versatile uncertainty-aware visualization tool can be easily adapted to other scientific domains beyond single-cell RNA sequencing, making them valuable resources for high-dimensional data analysis.

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#	sentence	role	the authors' own work	states the main conclusion	strength
s0	Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately.	background 1.00	0.05	0.03	asserted 1.00
s1	However, t-SNE often fails to account for uncertainties in the original dataset, leading to misleading visualizations where cell subsets with noise appear indistinguishable.	gap 0.72	0.09	0.04	asserted 0.90
s2	To address these challenges, we introduce uncertainty-aware t-SNE (Ut-SNE), a noise-defending visualization tool tailored for uncertain single-cell RNA-seq data.	contribution 1.00	0.87	0.17	asserted 1.00
s3	By creating a probabilistic representation for each sample, Our Ut-SNE accurately incorporates noise about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data, revealing significant uncertainties in transcriptomic variability.	method 0.90	0.92	0.43	asserted 1.00
s4	Through various examples, we showcase the practical value of Ut-SNE and underscore the significance of incorporating uncertainty awareness into data visualization practices.	interpretation 0.39	0.89	0.23	asserted 1.00
s5	This versatile uncertainty-aware visualization tool can be easily adapted to other scientific domains beyond single-cell RNA sequencing, making them valuable resources for high-dimensional data analysis.	interpretation 0.48	0.25	0.17	asserted 0.95

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.70.

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Original abstract

Nonlinear data visualization using t-distributed stochastic neighbor embedding (t-SNE) enables the representation of complex single-cell transcriptomic landscapes in two or three dimensions to depict biological populations accurately. However, t-SNE often fails to account for uncertainties in the original dataset, leading to misleading visualizations where cell subsets with noise appear indistinguishable. To address these challenges, we introduce uncertainty-aware t-SNE (Ut-SNE), a noise-defending visualization tool tailored for uncertain single-cell RNA-seq data. By creating a probabilistic representation for each sample, Our Ut-SNE accurately incorporates noise about transcriptomic variability into the visual interpretation of single-cell RNA sequencing data, revealing significant uncertainties in transcriptomic variability. Through various examples, we showcase the practical value of Ut-SNE and underscore the significance of incorporating uncertainty awareness into data visualization practices. This versatile uncertainty-aware visualization tool can be easily adapted to other scientific domains beyond single-cell RNA sequencing, making them valuable resources for high-dimensional data analysis.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Lifting PCA in the Wasserstein Space
stat.ML 2026-10 conditional novelty 7.0 of 10

Lifted PCA turns PCA for distribution-valued data into a Wasserstein barycenter over Stiefel or Grassmann manifolds, recovering classical PCA and uncertainty-aware PCA as special cases.
Visualizing Uncertainty in Non-linear Projections with Ensembles
cs.HC 2026-08 conditional novelty 5.0 of 10

Ensembles of UMAP projections, especially median-based consensus embeddings and input perturbation, preserve projection quality while revealing run-to-run variability and overfitting.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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Other works aim to create dimensionality reduction techniques that can handle uncertain inputs [ref] , including uncertainty-aware versions of PCA [ref] , t-SNE [ref] , and MDS [ref] .

Visualizing Uncertainty in Non-linear Projections with Ensembles · Visualizing projection uncertainty · 0.87

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Visualizations generated by other nonlinear embedding algorithms, such as locally linear embedding (LLE) [ref] , multidimensional scaling (MDS) [ref] , self-organizing map (SOM) [ref] , and uniform manifold approximation and projection (UMAP) [ref] , Laplacian eigenmaps [ref] , Harmony, and Scanorama can be interpreted in much the same way.

UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction · Introduction · 0.87

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