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

TL;DR

Ut-SNE is a novel visualization method that incorporates uncertainty in single-cell RNA-seq data, improving the accuracy of visualizing complex biological populations by accounting for transcriptomic variability.

Contribution

This paper introduces Ut-SNE, the first t-SNE variant that explicitly models and visualizes uncertainty in single-cell transcriptomic data.

Findings

Ut-SNE reveals significant uncertainties in transcriptomic variability.

It improves the interpretability of single-cell RNA-seq visualizations.

The method is adaptable to other high-dimensional data domains.

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.