Misra Rohan, O'Leary Kevin, Chen Wenna, Zheng Deyou
Department of Genetics, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, New York, USA.
Department of Neurology, Albert Einstein College of Medicine, 1300 Morris Park Ave., Bronx, New York, USA.
bioRxiv. 2025 Sep 1:2025.08.27.672621. doi: 10.1101/2025.08.27.672621.
Visualization is widely used to help explore and interpret high dimensional single cell (sc) omics data, such as scRNA-seq expression data. In particular, uniform manifold approximation and projection (UMAP) has become nearly ubiquitous in scientific publications that apply single cell omics technologies. Some experts have expressed concerns that the global cell-cell relationship, especially the spatial distances among cell clusters in a dataset, may not be faithfully depicted in a 2-dimensional (2D) UMAP. To help users to better appreciate this issue with their own data, we created an online server for the community to upload their single cell data and interactively make UMAPs with different hyper-parameters to witness how the distribution of cell clusters changes. The server thus can help promote proper usages of UMAP, especially to avoid the common pitfalls in misinterpretation of inter-cluster relationships in single cell studies.
可视化被广泛用于帮助探索和解释高维单细胞（sc）组学数据，例如scRNA-seq表达数据。特别是，均匀流形近似与投影（UMAP）在应用单细胞组学技术的科学出版物中几乎无处不在。一些专家担心，二维（2D）UMAP可能无法如实地描绘全局细胞-细胞关系，尤其是数据集中细胞簇之间的空间距离。为了帮助用户更好地理解自己数据中的这个问题，我们创建了一个在线服务器，供社区上传他们的单细胞数据，并通过交互方式生成具有不同超参数的UMAP，以观察细胞簇的分布是如何变化的。因此，该服务器有助于促进UMAP的正确使用，特别是避免在单细胞研究中错误解释簇间关系的常见陷阱。
