Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data
<|reference_start|>Uncertainty-aware t-distributed Stochastic Neighbor Embedding for Single-cell RNA-seq Data: 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.<|reference_end|>
@article{ma2024uncertainty-aware,
    title={Uncertainty-aware t-distributed Stochastic Neighbor Embedding for
  Single-cell RNA-seq Data},
    author={Hui Ma and Kai Chen},
    journal={arXiv preprint arXiv:2410.00473},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.00473},
    primaryClass={q-bio.GN cs.LG}
}