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lilab-bcb / harmony-pytorch

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Harmony-Pytorch

|PyPI| |Conda| |Python| |License|

.. |PyPI| image:: https://img.shields.io/pypi/v/harmony-pytorch.svg :target: https://pypi.org/project/harmony-pytorch

.. |Conda| image:: https://img.shields.io/conda/v/bioconda/harmony-pytorch :target: https://anaconda.org/bioconda/harmony-pytorch

.. |Python| image:: https://img.shields.io/pypi/pyversions/harmony-pytorch.svg :target: https://pypi.org/project/harmony-pytorch

.. |License| image:: https://img.shields.io/github/license/lilab-bcb/harmony-pytorch :target: https://github.com/lilab-bcb/harmony-pytorch/blob/master/LICENSE

This is a Pytorch implementation of Harmony algorithm on single-cell sequencing data integration. Please see Ilya Korsunsky et al., 2019 <https://www.nature.com/articles/s41592-019-0619-0>_ for details.

Installation ^^^^^^^^^^^^^

This package is published on PyPI::

pip install harmony-pytorch

Usage ^^^^^^^^

General Case ##############

Given an embedding X as a N-by-d matrix in numpy array structure (N for number of cells, d for embedding components) and cell attributes as a Data Frame df_metadata, use Harmony for data integration as the following::

from harmony import harmonize
Z = harmonize(X, df_metadata, batch_key = 'Channel')

where Channel is the attribute in df_metadata for batches.

Alternatively, if there are multiple attributes for batches, write::

Z = harmonize(X, df_metadata, batch_key = ['Lab', 'Date'])

Input as MultimodalData Object ###############################

It's easy for Harmony-pytorch to work with count matrix data structure from PegasusIO <https://pegasusio.readthedocs.io>_ package. Let data be a MultimodalData object in Python::

from harmony import harmonize
Z = harmonize(data.obsm['X_pca'], data.obs, batch_key = 'Channel')
data.obsm['X_pca_harmony'] = Z

This will calculate the harmonized PCA matrix for the default UnimodalData of data.

Given a UnimodalData object unidata, you can also use the code above to perform Harmony algorithm: simply substitute unidata for data there.

Input as AnnData Object ##########################

It's easy for Harmony-pytorch to work with annotated count matrix data structure from anndata <https://icb-anndata.readthedocs-hosted.com/en/stable/index.html>_ package. Let adata be an AnnData object in Python::

from harmony import harmonize
Z = harmonize(adata.obsm['X_pca'], adata.obs, batch_key = '<your-batch-key>')
adata.obsm['X_harmony'] = Z

where <your-batch-key> should be replaced by the actual batch key attribute name in your data.

For details about AnnData data structure, please refer to its documentation <https://icb-anndata.readthedocs-hosted.com/en/stable/anndata.AnnData.html>_.