spateo.preprocessing.filter#
Filter functions.
Module Contents#
Functions#
|
Select valid cells based on a collection of filters. |
|
Select valid genes based on a collection of filters. |
|
Select valid cells by coordinates. |
- spateo.preprocessing.filter.filter_cells(adata: anndata.AnnData, filter_bool: numpy.ndarray | None = None, keep_filtered: bool = False, min_expr_genes: int = 50, max_expr_genes: float = np.inf, min_area: float = 0, max_area: float = np.inf, inplace: bool = False) anndata.AnnData | None[source]#
Select valid cells based on a collection of filters. This function is partially based on dynamo (https://github.com/aristoteleo/dynamo-release).
TODO: What layers need to be considered? Argument shared_count ?
- Parameters:
- adata
AnnData object.
- filter_bool
A boolean array from the user to select cells for downstream analysis.
- keep_filtered
Whether to keep cells that don’t pass the filtering in the adata object.
- min_expr_genes
Minimal number of genes with expression for a cell in the data from X.
- max_expr_genes
Maximal number of genes with expression for a cell in the data from X.
- min_area
Maximum area of a cell in the data from X.
- max_area
Maximum area of a cell in the data from X.
- inplace
Perform computation inplace or return result.
- Returns:
An updated AnnData object with pass_basic_filter as a new column in obs to indicate the selection of cells for downstream analysis. adata will be subset with only the cells pass filtering if keep_filtered is set to be False.
- spateo.preprocessing.filter.filter_genes(adata: anndata.AnnData, filter_bool: numpy.ndarray | None = None, keep_filtered: bool = False, min_cells: int = 1, max_cells: float = np.inf, min_avg_exp: float = 0, max_avg_exp: float = np.inf, min_counts: float = 0, max_counts: float = np.inf, inplace: bool = False) anndata.AnnData | None[source]#
Select valid genes based on a collection of filters. This function is partially based on dynamo (https://github.com/aristoteleo/dynamo-release).
- Parameters:
- adata
filter_bool:
ndarray(default: None) A boolean array from the user to select genes for downstream analysis.- keep_filtered
Whether to keep genes that don’t pass the filtering in the adata object.
- min_cells
Minimal number of cells with expression in the data from X.
- max_cells
Maximal number of cells with expression in the data from X.
- min_avg_exp
Minimal average expression across cells for the data.
- max_avg_exp
Maximal average expression across cells for the data.
- min_counts
Minimal number of counts (UMI/expression) for the data
- max_counts
Minimal number of counts (UMI/expression) for the data
- inplace
Perform computation inplace or return result.
- Returns:
An updated AnnData object with pass_basic_filter as a new column in var to indicate the selection of genes for downstream analysis. adata will be subset with only the genes pass filtering if keep_filtered is set to be False.
- spateo.preprocessing.filter.filter_by_coordinates(adata: anndata.AnnData, filter_bool: numpy.ndarray | None = None, keep_filtered: bool = False, x_range: Sequence[float] = (-np.inf, np.inf), y_range: Sequence[float] = (-np.inf, np.inf), inplace: bool = False) anndata.AnnData | None[source]#
Select valid cells by coordinates. TODO: lasso tool
- Parameters:
- adata
AnnData object.
- filter_bool
A boolean array from the user to select cells for downstream analysis.
- keep_filtered
Whether to keep cells that don’t pass the filtering in the adata object.
- x_range
The X-axis range of cell coordinates.
- y_range
The Y-axis range of cell coordinates.
- inplace
Perform computation inplace or return result.
- Returns:
An updated AnnData object with pass_basic_filter as a new column in obs to indicate the selection of cells for downstream analysis. adata will be subset with only the cells pass filtering if keep_filtered is set to be False.