Cell Layers: uncovering clustering structure in unsupervised single-cell transcriptomic analysis.
Bioinform Adv
; 2(1): vbac051, 2022.
Article
en En
| MEDLINE
| ID: mdl-35967929
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.
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MEDLINE
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En
Revista:
Bioinform Adv
Año:
2022
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Article
País de afiliación:
Estados Unidos