Pytorch implementation of Harmony algorithm on single-cell sequencing data integration
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.
This package is published on PyPI:
pip install harmony-pytorch
Given an embedding X as a N-by-d matrix (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 AnnData Object
It’s easy for Harmony-pytorch to work with annotated count matrix data structure from anndata package. Let adata be an AnnData object in Python:
from harmony import harmonize Z = harmonize(adata.obsm['X_pca'], adata.obs, batch_key = 'Channel') adata.obsm['X_harmony'] = Z
For details about AnnData data structure, please refer to its documentation.
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