tdm.tissue.Tissue#

class tdm.tissue.Tissue(single_cell_df: DataFrame, cell_types: list[str], tissue_dimensions: tuple[float, float] | None = None)[source]#

A tissue represents one real-life tissue section.

__add__(other: Tissue) → Tissue[source]#

Add two Tissue objects together by creating a new Tissue object with cells from both tissues.

Parameters:

other (Tissue) – The Tissue object to be added.

Returns:

A new Tissue object that is the result of the addition.

Return type:

Tissue

__init__(single_cell_df: DataFrame, cell_types: list[str], tissue_dimensions: tuple[float, float] | None = None)[source]#

Initialize the tissue from a subset of the single_cell_df corresponding with a single img_id (see Preprocessing).

Parameters:

  • single_cell_df (pd.DataFrame) – a subset of the single_cell_df corresponding with a single img_id (see: Preprocessing)

  • cell_types (list[str]) – list of supported cell types.

  • tissue_dimensions (tuple[float, float] | None, optional) – maximal x,y limits of the tissue section (assumes coordinates start at 0,0). Defaults to the maximal x,y values in the data.

cell_df() → DataFrame[source]#

Returns the subset of the single_cell_df containing information this tissue (see: Preprocessing)

Returns:

The DataFrame containing cell information.

Return type:

pd.DataFrame

cell_types() → list[str][source]#

List of cell types supported by this tissue.

Note

A tissue supporting a list of cell_types might not have an instance of each one of them. The reason is that we want to be clear which cell types are included in this analysis.

property img_id#

A unique identifier of the tissue.

n_cells(cell_type: str | None = None, neighborhood_size: float | None = None) → float[source]#

Number of cells in the tissue.

Note

Returns a float type only if result is scaled to a specific neighborhood size (i.e neighborhood_size is not None)

Parameters:

  • cell_type (str | None, optional) – return only the number of this cell type. Defaults to None.

  • neighborhood_size (float | None, optional) – scale the number of cells to match a neighborhood with the same density. Defaults to None.

Returns:

number of cells

Return type:

float

n_cells_df(neighborhood_size: float | None = None)[source]#
n_cells_list(neighborhood_size: float | None = None, cell_types: list[str] | None = None)[source]#
present_cell_types() → list[str][source]#

List of cell types with at least one cell in the tissue.

Returns:

List of cell types.

Return type:

list[str]

property subject_id#

A unique identifier of the patient.

tissue_dimensions() → tuple[float, float][source]#
Returns:

maximal x,y limits of the tissue section (assumes coordinates start at 0,0).

Return type:

(x_max, y_max)