FACSPy.tl.scanorama_integrate

FACSPy.tl.scanorama_integrate#

FACSPy.tl.scanorama_integrate(adata, gate, layer, key, basis='pca', adjusted_basis='scanorama', knn=20, sigma=15, approx=True, alpha=0.1, batch_size=5000, copy=False, **kwargs)#

Computes scanorama integration.

Parameters:

  • adata (AnnData) – The anndata object of shape n_obs x n_vars where rows correspond to cells and columns to the channels

  • gate (str) – The gate to be analyzed, called by the population name. This parameter has a default stored in fp.settings, but can be superseded by the user.

  • layer (str) – The layer corresponding to the data matrix. Similar to the gate parameter, it has a default stored in fp.settings which can be overwritten by user input.

  • key (str) – Column in .obs that specifies the batch.

  • basis (str) – Entry in .obsm that specifies the embedding.

  • adjusted_basis (str) – Name of the integrated embedding.

  • knn (int) – Number of nearest neighbors to use for matching.

  • sigma (float) – Correction smoothing parameter on Gaussian kernel.

  • approx (bool) – Use approximate nearest neighbors with Python annoy; greatly speeds up matching runtime.

  • alpha (float) – Alignment score minimum cutoff.

  • batch_size (int) –

    The batch size used in the alignment vector computation. Useful when integrating very large (>100k samples) datasets. Set to large value that runs within available memory.

    Return a copy of adata instead of modifying inplace.

  • **kwargs (dict, optional) – keyword arguments that are passed directly to the scanorama.scanorama function. Please refer to its documentation.

Returns:

Returns adata if copy = True, otherwise adds fields to the anndata object:

.obsm[adjusted_basis]

integrated embedding of the data

Return type:

AnnData or None

Examples

>>> import FACSPy as fp
>>> dataset
AnnData object with n_obs × n_vars = 615936 × 22
obs: 'sample_ID', 'file_name', 'condition', 'sex', 'batch'
var: 'pns', 'png', 'pne', 'pnr', 'type', 'pnn', 'cofactors'
uns: 'metadata', 'panel', 'workspace', 'gating_cols', 'dataset_status_hash'
obsm: 'gating'
layers: 'compensated', 'transformed'
>>> fp.settings.default_gate = "T_cells"
>>> fp.settings.default_layer = "transformed"
>>> fp.tl.pca(dataset)
>>> fp.tl.scanorama_integrate(
...     dataset,
...     basis = "X_pca_T_cells_transformed",
...     adjusted_basis = "X_pca_T_cells_transformed_scanorama",
... )
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