Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD 21205, USA.
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC 27710, USA.
Bioinformatics. 2022 Jul 11;38(14):3654-3656. doi: 10.1093/bioinformatics/btac368.
In the exploratory data analysis of single-cell or spatial genomic data, single-cells or spatial spots are often visualized using a two-dimensional plot where cell clusters or spot clusters are marked with different colors. With tens of clusters, current visualization methods often assign visually similar colors to spatially neighboring clusters, making it hard to identify the distinction between clusters. To address this issue, we developed Palo that optimizes the color palette assignment for single-cell and spatial data in a spatially aware manner. Palo identifies pairs of clusters that are spatially neighboring to each other and assigns visually distinct colors to those neighboring pairs. We demonstrate that Palo leads to improved visualization in real single-cell and spatial genomic datasets.
Palo R package is freely available at Github (https://github.com/Winnie09/Palo) and Zenodo (https://doi.org/10.5281/zenodo.6562505).
Supplementary data are available at Bioinformatics online.
在单细胞或空间基因组数据的探索性数据分析中，通常使用二维图来可视化单细胞或空间点，其中细胞簇或斑点簇用不同的颜色标记。对于数十个簇，当前的可视化方法通常会将视觉上相似的颜色分配给空间上相邻的簇，从而难以识别簇之间的区别。为了解决这个问题，我们开发了 Palo，它以空间感知的方式优化了单细胞和空间数据的调色板分配。Palo 识别彼此空间相邻的簇对，并为那些相邻的对分配视觉上明显不同的颜色。我们证明 Palo 可改善真实单细胞和空间基因组数据集的可视化效果。
Palo R 包可在 Github（https://github.com/Winnie09/Palo）和 Zenodo（https://doi.org/10.5281/zenodo.6562505）上免费获得。
补充数据可在生物信息学在线获得。
