Low-dimensional representations of high-dimensional data are routinely employed in biomedical research to visualize, interpret and communicate results from different pipelines. In this article, we propose a novel procedure to directly estimate t-SNE embeddings that are not driven by batch effects. Without correction, interesting structure in the data can be obscured by batch effects. The proposed algorithm can therefore significantly aid visualization of high-dimensional data.

中文翻译：

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用于批量校正的预计 t-SNE。


高维数据的低维表示通常用于生物医学研究中，以可视化、解释和传达来自不同管道的结果。在本文中，我们提出了一种新颖的过程来直接估计不受批次效应驱动的t -SNE 嵌入。如果不进行校正，数据中有趣的结构可能会因批次效应而变得模糊。因此，所提出的算法可以显着帮助高维数据的可视化。