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Palo: Spatially-aware color palette optimization for single-cell and spatial data.

Authors
  • Hou, Wenpin1
  • Ji, Zhicheng2
  • 1 Department of Biostatistics, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, 21205, USA.
  • 2 Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, NC, 27710, USA.
Type
Published Article
Journal
Bioinformatics (Oxford, England)
Publication Date
Jun 01, 2022
Identifiers
DOI: 10.1093/bioinformatics/btac368
PMID: 35642896
Source
Medline
Language
English
License
Unknown

Abstract

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). © The Author(s) 2022. Published by Oxford University Press.

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