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. 2023 Sep 26;39(10):btad594. doi: 10.1093/bioinformatics/btad594

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© The Author(s) 2023. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Figure 3. — Comparison of various spatial domain detection algorithms on the DLPFC dataset, sample 151673. (a) Manual annotation (ground truth). (b) Scribbles by a human annotator. (c) Spatial domains detected by ScribbleDom. (d) Spatial domains detected by SC-MEB. (e) Spatial domains detected by BayesSpace. (f) Spatial domains detected by AutoScribbleDom.