spateo.tools.spatial_impute.run_impute#

Wrapper function to run generative modeling for count denoising and imputation.

Module Contents#

Functions#

impute_and_downsample(→ Tuple[anndata.AnnData, ...)

Smooth gene expression distributions and downsample a spatial sample by selecting representative points from
spateo.tools.spatial_impute.run_impute.impute_and_downsample(adata: anndata.AnnData, filter_by_moran: bool = False, spatial_key: str = 'spatial', positive_ratio_cutoff: float = 0.1, imputation: bool = True, n_ds: Optional[int] = None, to_visualize: Union[None, str, List[str]] = None, cmap: str = 'magma', device: str = 'cpu', **kwargs) → Tuple[anndata.AnnData, anndata.AnnData][source]#

Smooth gene expression distributions and downsample a spatial sample by selecting representative points from this smoothed slice.

Parameters
adata

AnnData object to model

filter_by_moran

Set True to split - for samples with highly uniform expression patterns, simple spatial smoothing will be used. For samples with localized patterns, graph neural network will be used for smoothing. If False, graph neural network will be applied to all genes.

spatial_key

Only used if ‘filter_by_moran’ is True; key in .obsm where x- and y-coordinates are stored.

positive_ratio_cutoff

Filter condition for genes- each gene must be present in higher than this proportion of the total number of cells to be retained

imputation

Set True to perform imputation. If False, will only downsample.

n_ds

Optional number of cells to downsample to- if not given, will not perform downsampling

kwargs

Additional arguments that can be provided to :func STGNN.train_STGNN. Options for kwargs: - learn_rate: Float, controls magnitude of gradient for network learning - dropout: Float between 0 and 1, proportion of weights in each layer to set to 0 - act: String specifying activation function for each encoder layer. Options: “sigmoid”, “tanh”, “relu”,

”elu”

  • clip: Float between 0 and 1, threshold below which imputed feature values will be set to 0,

    as a percentile. Recommended between 0 and 0.1.

  • weight_decay: Float, controls degradation rate of parameters

  • epochs: Int, number of iterations of training loop to perform

  • dim_output: Int, dimensionality of the output representation

  • alpha: Float, controls influence of reconstruction loss in representation learning

  • beta: Float, weight factor to control the influence of contrastive loss in representation learning

  • theta: Float, weight factor to control the influence of the regularization term in representation learning

  • add_regularization: Bool, adds penalty term to representation learning

Returns

Input AnnData object (optional) adata_rex: (optional) adata: AnnData subsetted down to downsampled buckets.

Return type

adata_orig