https://github.com/dnbaker/minicore-experiments
Experiments for minicore: fast scRNA-seq clustering with various distances
https://github.com/dnbaker/minicore-experiments
Last synced: 10 months ago
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Experiments for minicore: fast scRNA-seq clustering with various distances
- Host: GitHub
- URL: https://github.com/dnbaker/minicore-experiments
- Owner: dnbaker
- Created: 2020-11-19T22:16:09.000Z (over 5 years ago)
- Default Branch: release
- Last Pushed: 2021-09-26T19:30:24.000Z (over 4 years ago)
- Last Synced: 2025-03-01T14:32:56.236Z (over 1 year ago)
- Language: Python
- Homepage:
- Size: 1.61 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
This repository contains experiments for `minicore: Fast scRNA-seq clustering with various distances`.
### Dependencies
1. sklearn
2. numpy
3. scipy
4. minicore v0.3
### Installing minicore
To install minicore's Python bindings, clone [minicore recursively](https://github.com/dnbaker/minicore), change into the Python directory and run `python3 setup.py install`.
Alternatively, a single-command install would be `python3 -m pip install git+git://github.com/dnbaker/minicore@main`. Please create an issue if you encounter any difficulties.
Because compilation can be somewhat slow, the build is parallelized by default with `$OMP_NUM_THREADS` threads. Run your command as `OMP_NUM_THREADS=number python3 setup.py install` to use `number` threads.
### Setting up data
Data can be downloaded in compressed CSR-format [here](https://doi.org/10.5281/zenodo.4738365).
For simplicity, simply run this [download script](https://raw.githubusercontent.com/dnbaker/minicore-experiments/release/download.sh),
which will download and decompress the tarballs necessary.
It will prompt you to append `MINICORE_DATA` to your .bashrc; if you choose not to, you simply need to export `export MINICORE_DATA=${PATH}` to the path where you downloaded minicore\_data. Then you should be able to perform these experiments.