Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis

MOTIVATION: Unsupervised clustering of single-cell transcriptomics is a powerful method for identifying cell populations. Static visualization techniques for single-cell clustering only display results for a single resolution parameter. Analysts will often evaluate more than one resolution parameter but then only report one. RESULTS: We developed Cell Layers, an interactive Sankey tool for the quantitative investigation of gene expression, co-expression, biological processes and cluster integrity across clustering resolutions. Cell Layers enhances the interpretability of single-cell clustering by linking molecular data and cluster evaluation metrics, providing novel insight into cell populations. AVAILABILITY AND IMPLEMENTATION: https://github.com/apblair/CellLayers.