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Anomaly detection in spatial transcriptomics via spatially localized density comparison
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DOI：10.1093/bioinformatics/btaf242.png)
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Abstract
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Motivation Perturbations in biological tissues-e.g. due to inflammation, disease, or drug treatment-alter the composition of cell types and cell states in the tissue. These alterations are often spatially localized in different regions of a tissue, and can be measured using spatial transcriptomics technologies. However, current methods to analyze differential abundance in cell types or cell states, either do not incorporate spatial information-and thus cannot identify spatially localized alterations-or use heuristic and inaccurate approaches.Results We introduce Spatial Anomaly Region Detection in Expression Manifolds (Sardine), a method to estimate spatially localized changes in spatial transcriptomics data obtained from tissue slices from two or more conditions. Sardine estimates the probability of a cell state being at the same (relative) spatial location between different conditions using spatially localized density estimation. On simulated data, Sardine recapitulates the spatial patterning of expression changes more accurately than existing approaches. On a Visium dataset of the mouse cerebral cortex before and after injury response, as well as on a Visium dataset of a mouse spinal cord undergoing electrotherapy, Sardine identifies regions of spatially localized expression changes that are more biologically plausible than alternative approaches.Availability and implementation We implement Sardine in Python 3, with an open source implementation available at: https://github.com/raphael-group/spatial_anomaly_detection.
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