API Reference#
Config#
Global configurations for spatialtis. |
Cell quantification from Images#
Read single cell data from images and masks. |
Read from 10x visium#
Read visium data from visium result folders. |
WKT Format helper#
Transform normal coordination in AnnData.obs to wkt-format. |
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Transform normal coordination in AnnData.obs to wkt-format. |
IO#
Read spatialtis result from AnnData.uns as pandas.DataFrame object. |
Basic analysis#
Count the proportion of each types of cells in each group. |
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Calculating cell density in each ROI. |
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Cell morphology variation between different groups. |
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The likelihood of two type of cells occur simultaneously in a ROI. |
Spatial analysis#
To find the neighbors of each cell. |
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A generator that return spatial weight in CSR matrix |
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Cell distribution pattern |
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Profiling cell-cell interaction using permutation test |
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Spatial communities detection |
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Evaluate tissue heterogeneity based on entropy |
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Spatial auto-correlation for every marker. |
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Profiling markers spatial enrichment using permutation test |
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Identifying spatial co-expression markers using correlation |
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Identify neighbor cells dependent marker |
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Identify neighbor markers dependent marker |
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This is a wrapper around somde |
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A pytorch reimplementation of GCNG |
Base class#
The base class for all analysis function |
To use plotting, import it in following schema:
>>> import spatialtis.plotting as sp
ROI Visualization#
Visualize cells and neighbors relationship in ROI |
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Visualize marker expression in ROI |
Analysis Visualization#
Visualization of the cell components |
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Visualization of cell density |
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Visualization of cell morphology |
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Visualization of cell co-occurrence |
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Visualization of spatial heterogeneity analysis |
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Visualization of the cell interaction analysis |
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Visualization of the spatial enrichment analysis |