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The art of using t-SNE for single-cell transcriptomics

View ORCID ProfileDmitry Kobak, View ORCID ProfilePhilipp Berens
doi: https://doi.org/10.1101/453449
Dmitry Kobak
1Institute for Ophthalmic Research, University of Tübingen, Germany
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Philipp Berens
1Institute for Ophthalmic Research, University of Tübingen, Germany
2Department of Computer Science, University of Tübingen, Germany
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  • For correspondence: philipp.berens@uni-tuebingen.de
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Abstract

Single-cell transcriptomics yields ever growing data sets containing RNA expression levels for thousands of genes from up to millions of cells. Common data analysis pipelines include a dimensionality reduction step for visualising the data in two dimensions, most frequently performed using t-distributed stochastic neighbour embedding (t-SNE). It excels at revealing local structure in high-dimensional data, but naive applications often suffer from severe shortcomings, e.g. the global structure of the data is not represented accurately. Here we describe how to circumvent such pitfalls, and develop a protocol for creating more faithful t-SNE visualisations. It includes PCA initialisation, a high learning rate, and multi-scale similarity kernels; for very large data sets, we additionally use exaggeration and downsampling-based initialisation. We use published single-cell RNA-seq data sets to demonstrate that this protocol yields superior results compared to the naive application of t-SNE.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted May 21, 2019.
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The art of using t-SNE for single-cell transcriptomics
Dmitry Kobak, Philipp Berens
bioRxiv 453449; doi: https://doi.org/10.1101/453449
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The art of using t-SNE for single-cell transcriptomics
Dmitry Kobak, Philipp Berens
bioRxiv 453449; doi: https://doi.org/10.1101/453449

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