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. 2022 Jun 27;2(6):387–398. doi: 10.1038/s43588-022-00263-8

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© The Author(s) 2022, corrected publication 2022

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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Extended Data Fig. 1 — The results of classification where cells are labeled according to fields of view (FOV). (a-b) test accuracy with min-complexity strategy, where comparison methods use the same number of cells as ActiveSVM in (a) and use the entire dataset in (b). Fields of view 1-5 correspond to 5 regions of the mouse cortex, additional fields of view are labeled SVZ (subventricular zone) and ChP (choroid plexus). (c) t SNE of cell transcriptomes for all cells (d) number of cells used per iteration (e) Sample of identified genes where each sub-panel shows mean expression across FOV/brain regions for selected gene, a tSNE plot colored by expression of selected gene, a violin plot of single cell gene expression values for selected gene in FOV/brain region, and spatial plots of each field of view where dots represents cells in 2D imaging slice, cells are colored by intensity of selected gene and units are in millimeters.

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