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

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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.\nAVAILABILITY AND IMPLEMENTATION: Palo R package is freely available at Github (https://github.com/Winnie09/Palo) and Zenodo (https://doi.org/10.5281/zenodo.6562505).\nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.", "claims": [{"id": "claim-method", "statement": "To address this issue, we developed Palo to optimize the color palette assignments to cell or spot clusters in a spatially aware manner.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35642896", "locator": "sections[1] \"1 Introduction\" paragraph 6", "quote": "To address this issue, we developed Palo to optimize the color palette assignments to cell or spot clusters in a spatially aware manner."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The T cell single-cell RNA-seq dataset was obtained from Gene Expression Omnibus (GSE176022). 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