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Cell Layers: Uncovering clustering structure and knowledge in unsupervised single-cell transcriptomic analysis

By A.P. Blair, Robert K. Hu, Elie N. Farah, Katherine Pollard, Pawel F. Przytycki, Irfan S. Kathiriya, Benoit G. Bruneau

Posted 30 Nov 2020
bioRxiv DOI: 10.1101/2020.11.29.400614

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, coexpression, 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, to provide novel insight into cell populations. Availability and implementation: Upon request. Keywords: single-cell RNA sequencing, clustering, Plotly, Sankey, Ternary, gene expression. Contact: andrew.blair@gladstone.ucsf.edu and irfan.kathiriya@ucsf.edu .

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