The DOI matches its Crossref record; 18 of the 18 references it cites were found in a registry or an index.
The art of using t-SNE for single-cell transcriptomics
Open accessAbstract
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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SCANPY: large-scale single-cell gene expression data analysis
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Cited by
UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction
McInnes, L., Healy, J., & Melville, J. (2018) · arXiv · doi:10.48550/arxiv.1802.03426 · Cited by 7,101
A guide to machine learning for biologists
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Interpretable machine learning: Fundamental principles and 10 grand challenges
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A multimodal cell census and atlas of the mammalian primary motor cortex
BRAIN Initiative Cell Census Network (BICCN), BRAIN Initiative Cell Census Network (BICCN) Corresponding authors, Callaway, E. M., Dong, H.-W., Ecker, J. R., Hawrylycz, M. J., Huang, Z. J., Lein, E. S., Ngai, J., Osten, P., et al. (2021) · Nature · doi:10.1038/s41586-021-03950-0 · Cited by 522
Self-citation
Metadata
- Authors
- Kobak, D., & Berens, P.
- Venue
- Nature Communications
- Published
- 2019-11-28
- Volume
- 10
- Issue
- 1
- Pages
- 5416-5416
- ISSN
- 2041-1723 (ISSN-L)
- OpenAlex
- W2989747508
- Cited by
- 1,115
- References
- 18
- FWCI
- 30.22
- Type
- journal article
- Language
- en
- Open access
- gold
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