harmony-pytorch 0.1.0

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.

Installation

This package is published on PyPI:

pip install harmony-pytorch

Usage

General Case

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.