spateo.segmentation.icell#
Identify cells from RNA signal. Functions in this file are used to generate a cell mask, NOT to identify individual cells.
Original author @HailinPan, refactored by @Lioscro.
Module Contents#
Functions#
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Create a boolean mask indicating cells from stained image. |
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Create a boolean mask indicating nuclei from stained nuclei image. |
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Create a boolean mask indicating cells from stained image. |
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Create a boolean mask indicating nuclei from stained nuclei image, and |
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Calculate initial estimates for the negative binomial mixture. |
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Score each pixel by how likely it is a cell. Values returned are in |
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Score and mask pixels by how likely it is occupied. |
- spateo.segmentation.icell._mask_cells_from_stain(X: numpy.ndarray, otsu_classes: int = 3, otsu_index: int = 0, mk: int = 7) numpy.ndarray[source]#
Create a boolean mask indicating cells from stained image.
- spateo.segmentation.icell._mask_nuclei_from_stain(X: numpy.ndarray, otsu_classes: int = 3, otsu_index: int = 0, local_k: int = 55, offset: int = 5, mk: int = 5) numpy.ndarray[source]#
Create a boolean mask indicating nuclei from stained nuclei image. See
mask_nuclei_from_stain()for arguments.
- spateo.segmentation.icell.mask_cells_from_stain(adata: anndata.AnnData, otsu_classes: int = 3, otsu_index: int = 0, mk: int = 7, layer: str = SKM.STAIN_LAYER_KEY, out_layer: str | None = None)[source]#
Create a boolean mask indicating cells from stained image.
- Parameters:
- adata
Input Anndata
- otsu_classes
Number of classes to assign pixels to for cell detection
- otsu_index
Which threshold index should be used for classifying cells. All pixel intensities >= the value at this index will be classified as cell.
- mk
Size of the kernel used for morphological close and open operations applied at the very end.
- layer
Layer that contains staining image.
- out_layer
Layer to put resulting nuclei mask. Defaults to {layer}_mask.
- spateo.segmentation.icell.mask_nuclei_from_stain(adata: anndata.AnnData, otsu_classes: int = 3, otsu_index: int = 0, local_k: int = 55, offset: int = 5, mk: int = 5, layer: str = SKM.STAIN_LAYER_KEY, out_layer: str | None = None)[source]#
Create a boolean mask indicating nuclei from stained nuclei image, and save this mask in the AnnData as an additional layer.
- Parameters:
- adata
Input Anndata
- otsu_classes
Number of classes to assign pixels to for background detection.
- otsu_index
Which threshold index should be used for background. All pixel intensities less than the value at this index will be classified as background.
- local_k
The size of the local neighborhood of each pixel to use for local (adaptive) thresholding to identify the foreground (i.e. nuclei).
- offset
Offset to local thresholding values such that values > 0 lead to more “strict” thresholding, and therefore may be helpful in distinguishing nuclei in dense regions.
- mk
Size of the kernel used for morphological close and open operations applied at the very end.
- layer
Layer containing nuclei staining
- out_layer
Layer to put resulting nuclei mask. Defaults to {layer}_mask.
- spateo.segmentation.icell._initial_nb_params(X: numpy.ndarray, bins: numpy.ndarray | None = None) Dict[str, Tuple[float, float]] | Dict[int, Dict[str, Tuple[float, float]]][source]#
Calculate initial estimates for the negative binomial mixture.
- Parameters:
- X
UMI counts
- bins
Density bins
- Returns:
Dictionary containing initial w, mu, var parameters. If bins is also provided, the dictionary is nested with the outer dictionary containing each bin label as the keys. If zero_inflated=True, then the dictionary also contains a z key.
- spateo.segmentation.icell._score_pixels(X: scipy.sparse.spmatrix | numpy.ndarray, k: int, method: typing_extensions.Literal[gauss, spateo.segmentation.moran, EM, EM + gauss, EM + BP, VI + gauss, VI + BP], moran_kwargs: dict | None = None, em_kwargs: dict | None = None, vi_kwargs: dict | None = None, bp_kwargs: dict | None = None, certain_mask: numpy.ndarray | None = None, bins: numpy.ndarray | None = None) numpy.ndarray[source]#
Score each pixel by how likely it is a cell. Values returned are in [0, 1].
- Parameters:
- X
UMI counts per pixel as either a sparse or dense array.
- k
Kernel size for convolution.
- method
Method to use. Valid methods are: gauss: Gaussian blur moran: Moran’s I based method EM: EM algorithm to estimate cell and background expression
parameters.
EM+gauss: Negative binomial EM algorithm followed by Gaussian blur. EM+BP: EM algorithm followed by belief propagation to estimate the
marginal probabilities of cell and background.
- VI+gauss: Negative binomial VI algorithm followed by Gaussian blur.
Note that VI also supports the zero-inflated negative binomial (ZINB) by providing zero_inflated=True.
- VI+BP: VI algorithm followed by belief propagation. Note that VI also
supports the zero-inflated negative binomial (ZINB) by providing zero_inflated=True.
- moran_kwargs
Keyword arguments to the
moran.run_moran()function.- em_kwargs
Keyword arguments to the
em.run_em()function.- bp_kwargs
Keyword arguments to the
bp.run_bp()function.- certain_mask
A boolean Numpy array indicating which pixels are certain to be occupied, a-priori. For example, if nuclei staining is available, this would be the nuclei segmentation mask.
- bins
Pixel bins to segment separately. Only takes effect when the EM algorithm is run.
- Returns:
[0, 1] score of each pixel being a cell.
- Raises:
SegmentationError – If bins and/or certain_mask was provided but their sizes do not match X
- spateo.segmentation.icell.score_and_mask_pixels(adata: anndata.AnnData, layer: str, k: int, method: typing_extensions.Literal[gauss, spateo.segmentation.moran, EM, EM + gauss, EM + BP, VI + gauss, VI + BP], moran_kwargs: dict | None = None, em_kwargs: dict | None = None, vi_kwargs: dict | None = None, bp_kwargs: dict | None = None, threshold: float | None = None, use_knee: bool | None = False, mk: int | None = None, bins_layer: typing_extensions.Literal[False] | str | None = None, certain_layer: str | None = None, scores_layer: str | None = None, mask_layer: str | None = None)[source]#
Score and mask pixels by how likely it is occupied.
- Parameters:
- adata
Input Anndata
- layer
Layer that contains UMI counts to use
- k
Kernel size for convolution
- method
Method to use to obtain per-pixel scores. Valid methods are: gauss: Gaussian blur moran: Moran’s I based method EM: EM algorithm to estimate cell and background expression
parameters.
EM+gauss: Negative binomial EM algorithm followed by Gaussian blur. EM+BP: EM algorithm followed by belief propagation to estimate the
marginal probabilities of cell and background.
- VI+gauss: Negative binomial VI algorithm followed by Gaussian blur.
Note that VI also supports the zero-inflated negative binomial (ZINB) by providing zero_inflated=True.
- VI+BP: VI algorithm followed by belief propagation. Note that VI also
supports the zero-inflated negative binomial (ZINB) by providing zero_inflated=True.
- moran_kwargs
Keyword arguments to the
moran.run_moran()function.- em_kwargs
Keyword arguments to the
em.run_em()function.- bp_kwargs
Keyword arguments to the
bp.run_bp()function.- threshold
Score cutoff, above which pixels are considered occupied. By default, a threshold is automatically determined by using Otsu thresholding.
- use_knee
Whether to use knee point as threshold. By default is False. If True, threshold would be ignored.
- mk
Kernel size of morphological open and close operations to reduce noise in the mask. Defaults to k`+2 if EM or VI is run. Otherwise, defaults to `k-2.
- bins_layer
Layer containing assignment of pixels into bins. Each bin is considered separately. Defaults to {layer}_bins. This can be set to False to disable binning, even if the layer exists.
- certain_layer
Layer containing a boolean mask indicating which pixels are certain to be occupied. If the array is not a boolean array, it is casted to boolean.
- scores_layer
Layer to save pixel scores before thresholding. Defaults to {layer}_scores.
- mask_layer
Layer to save the final mask. Defaults to {layer}_mask.