# scanpy.external.pp.harmony_integrate¶

scanpy.external.pp.harmony_integrate(adata, key, basis='X_pca', adjusted_basis='X_pca_harmony', **kwargs)

Use harmonypy [Korunsky19] to integrate different experiments.

Harmony [Korunsky19] is an algorithm for integrating single-cell data from multiple experiments. This function uses the python port of Harmony, harmonypy, to integrate single-cell data stored in an AnnData object. As Harmony works by adjusting the principal components, this function should be run after performing PCA but before computing the neighbor graph, as illustrated in the example below.

Parameters
adata : AnnDataAnnData

The annotated data matrix.

key : strstr

The name of the column in adata.obs that differentiates among experiments/batches.

basis : strstr (default: 'X_pca')

The name of the field in adata.obsm where the PCA table is stored. Defaults to 'X_pca', which is the default for sc.tl.pca().

adjusted_basis : strstr (default: 'X_pca_harmony')

The name of the field in adata.obsm where the adjusted PCA table will be stored after running this function. Defaults to X_pca_harmony.

kwargs

Any additional arguments will be passed to harmonypy.run_harmony().

Returns

Updates adata with the field adata.obsm[obsm_out_field], containing principal components adjusted by Harmony such that different experiments are integrated.

Example

First, load libraries and example dataset, and preprocess.

>>> import scanpy as sc
>>> import scanpy.external as sce

We now arbitrarily assign a batch metadata variable to each cell for the sake of example, but during real usage there would already be a column in adata.obs giving the experiment each cell came from.
>>> adata.obs['batch'] = 1350*['a'] + 1350*['b']

Finally, run harmony. Afterwards, there will be a new table in adata.obsm containing the adjusted PC’s.
>>> sce.pp.harmony_integrate(adata, 'batch')