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.

翻译：由于样本顺序到达、实验条件变化以及知识不断演进，对连续且多样化的单细胞RNA测序（scRNA-seq）数据演化结构进行持续可视化的需求变得不可或缺。然而，作为scRNA-seq最先进的可视化与分析方法之一，t分布随机邻域嵌入（t-SNE）仅能离线可视化静态scRNA-seq数据，难以充分满足这一需求。为应对这些挑战，我们提出在线t-SNE方法，以实现对连续scRNA-seq数据的无缝整合。该方法通过利用旧样本的嵌入空间、探索新样本的嵌入空间，并实时对齐这两个嵌入空间来实现这一目标。因此，在线t-SNE能够显著支持新结构的持续发现，并在无需从头重新训练的情况下实现新scRNA-seq数据的高质量可视化。我们在多种连续scRNA-seq数据集上展示了在线t-SNE强大的可视化能力。