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Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis
Abstract
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.Many UC-authored scholarly publications are freely available on this site because of the UC's open access policies. Let us know how this access is important for you.