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Updated: Sep 1, 2025
Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
Published on: January 10, 2019
Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis
Andrew P Blair1, Robert K Hu2, Elie N Farah2
1Biological and Medical Informatics Graduate Program, University of California, San Francisco, CA 94143, USA.
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.
