https://github.com/berenslab/rna-seq-tsne
The art of using t-SNE for single-cell transcriptomics
Science Score: 10.0%
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Links to: nature.com -
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○Scientific vocabulary similarity
Low similarity (7.9%) to scientific vocabulary
Repository
The art of using t-SNE for single-cell transcriptomics
Basic Info
- Host: GitHub
- Owner: berenslab
- License: gpl-3.0
- Language: Jupyter Notebook
- Default Branch: master
- Size: 87.1 MB
Statistics
- Stars: 121
- Watchers: 10
- Forks: 34
- Open Issues: 2
- Releases: 0
Metadata Files
README.md
The art of using t-SNE for single-cell transcriptomics
This is a companion repository to our paper https://www.nature.com/articles/s41467-019-13056-x (Kobak & Berens 2019, The art of using t-SNE for single-cell transcriptomics). All code is in Python Jupyter notebooks. We used this t-SNE implementation: https://github.com/KlugerLab/FIt-SNE.
See demo.ipynb for a step-by-step guide using a data set from Tasic et al., Nature 2018 (24,000 cells sequenced with Smart-seq2).
The preprocessed data from Tasic et al. (after library size normalization, log-transformation, highly variable gene selection and reduction to 50 dimensions with PCA) are available in data/tasic-preprocessed as the 50-dimensional data matrix and an array of point colors.
The other notebooks generate all figures that we have in the paper:
toy-example.ipynbtasic-et-al.ipynbumi-datasets.ipynbmillion-cells.ipynbtwo-million-cells.ipynbumap-comparison.ipynb
The last three notebooks require one to run server-10xdata.py and server-cao.py. One needs more than 32 Gb of RAM to process these datasets conveniently, so these Python scripts were run separately on a powerful machine. They pickle all the results (t-SNE embeddings). Unfortunately, these pickles are too large to be shared on Github.
Owner
- Name: Berens Lab @ University of Tübingen
- Login: berenslab
- Kind: organization
- Email: philipp.berens@uni-tuebingen.de
- Location: Tübingen, Germany
- Website: https://hertie.ai/data-science
- Repositories: 60
- Profile: https://github.com/berenslab
Department of Data Science at the Hertie Institute for AI in Brain Health, University of Tübingen
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