pegasus.run_scanorama

pegasus.run_scanorama(data, batch='Channel', n_components=50, features='highly_variable_features', standardize=True, max_value=10.0, random_state=0)[source]

Batch correction using Scanorama.

This is a wrapper of Scanorama package. See [Hie19] for details on the algorithm.

Parameters

  • data (MultimodalData.) – Annotated data matrix with rows for cells and columns for genes.

  • batch (str, optional, default: "Channel".) – Which attribute in data.obs field represents batches, default is “Channel”.

  • n_components (int, optional default: 50.) – Number of integrated embedding components to keep. This sets Scanorama’s dimred parameter.

  • features (str, optional, default: "highly_variable_features".) – Keyword in data.var to specify features used for Scanorama.

  • standardize (bool, optional, default: True.) – Whether to scale the data to unit variance and zero mean.

  • max_value (float, optional, default: 10.) – The threshold to truncate data after scaling. If None, do not truncate.

  • random_state (int, optional, default: 0.) – Seed for random number generator.

Return type

str

Returns

  • out_rep (str) – The keyword in data.obsm referring to the embedding calculated by Scanorama algorithm. out_rep is always equal to “scanorama”

  • Update data.obsm –

    • data.obsm['X_scanorama']: The embedding calculated by Scanorama algorithm.

Examples

>>> pg.run_scanorama(data, random_state = 25)