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 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.