Nonparametric dimensionality reduction techniques, such as t-distributed stochastic neighbor embedding (t-SNE), are the most frequently used methods in the exploratory analysis of single-cell data sets. Current implementations scale poorly to massive data sets and often require downsampling or interpolative approximations, which can leave less frequent populations undiscovered and much information unexploited.

中文翻译：

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qSNE：二次速率 t-SNE 优化器，具有针对大型数据集的自动参数调整功能。

非参数降维技术，例如 t 分布随机邻域嵌入 (t-SNE)，是单细胞数据集探索性分析中最常用的方法。当前的实现对于海量数据集的扩展性很差，并且通常需要下采样或插值近似，这可能导致不太频繁的群体未被发现，并且许多信息未被利用。