Clustering trees for visualising scRNA-seq data

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Clustering trees for visualising scRNA-seq data Luke Zappia @_lazappi_

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Zappia L, Phipson B, Oshlack A. 2018. DOI:10.1371/journal.pcbi.1006245

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Many clustering tools > 25% of all tools Data from www.scRNA-tools.org

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How many clusters?

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A tree of clusters?

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Weighting edges In proportion = Number of cells on edge Number of cells in high res cluster

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Some examples

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My data iPSCs organoid

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NPHS1

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NPHS1

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Cell cycle SC3 stability Number of genes

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t-SNE 2 t-SNE 1 t-SNE 1 t-SNE 2

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Summary Choosing the number of clusters is hard but important A clustering tree can help by showing: - Relationships between clusters - Which clusters are distinct - Where samples are changing Compact, information dense visualisation - Alternative to t-SNE plots (or similar)

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Acknowledgements Everyone that makes tools and data available MCRI Bioinformatics Belinda Phipson MCRI KDDR Alex Combes @_lazappi_ oshlacklab.com lazappi.github.io/clustree Paper doi.org/10.1093/gigascience/giy083 Slides tidyurl.com/clustree-OzSingleCells Supervisors Alicia Oshlack Melissa Little