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harmonypy
=========
[![Latest PyPI Version][pb]][pypi] [![PyPI Downloads][db]][pypi] [![tests][gb]][yml] [](https://zenodo.org/badge/latestdoi/229105533)
[gb]: https://github.com/slowkow/harmonypy/actions/workflows/python-package.yml/badge.svg
[yml]: https://github.com/slowkow/harmonypy/actions/workflows/python-package.yml
[pb]: https://img.shields.io/pypi/v/harmonypy.svg
[pypi]: https://pypi.org/project/harmonypy/
[db]: https://img.shields.io/pypi/dm/harmonypy?label=pypi%20downloads
Harmony is an algorithm for integrating multiple high-dimensional datasets.
harmonypy is a port of the [harmony] R package by [Ilya Korsunsky].
Example
-------
<p align="center">
<img src="https://i.imgur.com/lqReopf.gif">
</p>
This animation shows the Harmony alignment of three single-cell RNA-seq datasets from different donors.
[→ How to make this animation.](https://slowkow.com/notes/harmony-animation/)
Installation
------------
This package has been tested with Python 3.7.
Use [pip] to install:
```bash
pip install harmonypy
```
Usage
-----
Here is a brief example using the data that comes with the R package:
```python
# Load data
import pandas as pd
meta_data = pd.read_csv("data/meta.tsv.gz", sep = "\t")
vars_use = ['dataset']
# meta_data
#
# cell_id dataset nGene percent_mito cell_type
# 0 half_TGAAATTGGTCTAG half 3664 0.017722 jurkat
# 1 half_GCGATATGCTGATG half 3858 0.029228 t293
# 2 half_ATTTCTCTCACTAG half 4049 0.015966 jurkat
# 3 half_CGTAACGACGAGAG half 3443 0.020379 jurkat
# 4 half_ACGCCTTGTTTACC half 2813 0.024774 t293
# .. ... ... ... ... ...
# 295 t293_TTACGTACGACACT t293 4152 0.033997 t293
# 296 t293_TAGAATTGTTGGTG t293 3097 0.021769 t293
# 297 t293_CGGATAACACCACA t293 3157 0.020411 t293
# 298 t293_GGTACTGAGTCGAT t293 2685 0.027846 t293
# 299 t293_ACGCTGCTTCTTAC t293 3513 0.021240 t293
data_mat = pd.read_csv("data/pcs.tsv.gz", sep = "\t")
data_mat = np.array(data_mat)
# data_mat[:5,:5]
#
# array([[ 0.0071695 , -0.00552724, -0.0036281 , -0.00798025, 0.00028931],
# [-0.011333 , 0.00022233, -0.00073589, -0.00192452, 0.0032624 ],
# [ 0.0091214 , -0.00940727, -0.00106816, -0.0042749 , -0.00029096],
# [ 0.00866286, -0.00514987, -0.0008989 , -0.00821785, -0.00126997],
# [-0.00953977, 0.00222714, -0.00374373, -0.00028554, 0.00063737]])
# meta_data.shape # 300 cells, 5 variables
# (300, 5)
#
# data_mat.shape # 300 cells, 20 PCs
# (300, 20)
# Run Harmony
import harmonypy as hm
ho = hm.run_harmony(data_mat, meta_data, vars_use)
# Write the adjusted PCs to a new file.
res = pd.DataFrame(ho.Z_corr)
res.columns = ['X{}'.format(i + 1) for i in range(res.shape[1])]
res.to_csv("data/adj.tsv.gz", sep = "\t", index = False)
```
[harmony]: https://github.com/immunogenomics/harmony
[Ilya Korsunsky]: https://github.com/ilyakorsunsky
[pip]: https://pip.readthedocs.io/
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