spatialtis_core.geo_analysis#

Module Contents#

spatialtis_core.geo_analysis.getis_ord(points: spatialtis_core.types.Points, bbox: spatialtis_core.types.BoundingBox, search_level: int = 3, quad: Optional[Tuple[int, int]] = None, rect_side: Optional[Tuple[float, float]] = None, pval: float = 0.05, min_cells: int = 10) List[bool]#

Getis-ord analysis to find hot cells

Parameters
  • points – A list of points

  • bbox – The bounding box

  • search_level – The level of outer-ring to search for

  • quad – eg.(X, Y) Use X * Y grid to perform analysis

  • rect_side – eg.(X, Y) Use X * Y rectangle to perform analysis

  • pval – The threshold for p-value

  • min_cells – The minimum number of cells to perform analysis

Returns

A list of bool

spatialtis_core.geo_analysis.spatial_autocorr(x: numpy.ndarray, neighbors: spatialtis_core.types.Neighbors, labels: spatialtis_core.types.Labels, two_tailed: bool = True, pval: float = 0.05, method: str = 'moran_i') List[Tuple[float, float]]#

Compute spatial auto-correlation value for a 2D array in parallel

The p-value is under the assumption of normal distribution Return is tuples of (spatial_autocorr value, p value)

Parameters
  • x – Gene expression matrix, each row is the expression of a gene

  • neighbors – A list of neighbors

  • labels – A list of labels

  • two_tailed – Determine the p value

  • pval – The p-value threshold

  • method – “moran_i” or “geary_c”

Returns

A list of (value, p_value)

spatialtis_core.geo_analysis.spatial_distribution_pattern(points_collections: List[spatialtis_core.types.Points], bbox: spatialtis_core.types.BoundingBox, method: str = 'id', r: Optional[float] = None, resample: int = 1000, quad: Optional[Tuple[int, int]] = None, rect_side: Optional[Tuple[float, float]] = None, pval: float = 0.05, min_cells: int = 10, dims: int = 2) List[Tuple[float, float, int]]#

Compute the distribution index and determine the pattern for different cells in a ROI in parallel

If data is 3D, only method=”id” is supported

Parameters
  • points_collections – A list of list of points

  • bbox – The bounding box

  • method – “id” for index of dispersion, “morisita” for morisita index, “clark_evans” for clark evans’ index

  • r – If method == “id”; The sample windows’ radius

  • resample – If method == “id”; The number of sampling times

  • quad – If method == “morisita”; eg.(X, Y) Use X * Y grid to perform analysis

  • rect_side – If method == “morisita”; eg.(X, Y) Use X * Y rectangle to perform analysis

  • pval – The threshold for p-value

  • min_cells – The minimum number of cells to perform analysis

  • dims – The dimension of data

Returns

A list of (index_value, p_value, pattern)

spatialtis_core.geo_analysis.spatial_entropy(points_collections: List[spatialtis_core.types.Points], types_collections: List[List[str]], method: str = 'leibovici', d: Optional[float] = None, cut: int = 3, dims: int = 2) List[float]#

Compute spatial entropy value of multiple ROIs in parallel

Parameters
  • points_collections – A list of list of points

  • types_collections – A list of list of types

  • bbox – The bounding box

  • method – “leibovici” or “altieri”

  • d – If method == “leibovici”; The distance threshold to determine co-occurrence

  • cut – If method == “altieri”; The distance interval to determine co-occurrence

  • dims – The dimension of data

Returns

A list of spatial entropy