Online t-SNE for single-cell RNA-seq
<|reference_start|>Online t-SNE for single-cell RNA-seq: Due to the sequential sample arrival, changing experiment conditions, and evolution of knowledge, the demand to continually visualize evolving structures of sequential and diverse single-cell RNA-sequencing (scRNA-seq) data becomes indispensable. However, as one of the state-of-the-art visualization and analysis methods for scRNA-seq, t-distributed stochastic neighbor embedding (t-SNE) merely visualizes static scRNA-seq data offline and fails to meet the demand well. To address these challenges, we introduce online t-SNE to seamlessly integrate sequential scRNA-seq data. Online t-SNE achieves this by leveraging the embedding space of old samples, exploring the embedding space of new samples, and aligning the two embedding spaces on the fly. Consequently, online t-SNE dramatically enables the continual discovery of new structures and high-quality visualization of new scRNA-seq data without retraining from scratch. We showcase the formidable visualization capabilities of online t-SNE across diverse sequential scRNA-seq datasets.<|reference_end|>
@article{ma2024online,
    title={Online t-SNE for single-cell RNA-seq},
    author={Hui Ma and Kai Chen},
    journal={arXiv preprint arXiv:2406.14842},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.14842},
    primaryClass={q-bio.GN cs.HC}
}