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. 2020 Nov 12;598(7879):144–150. doi: 10.1038/s41586-020-2907-3

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© The Author(s) 2020

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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Fig. 3 — a, t-SNE embedding constructed using electrophysiological features. Colour corresponds to t-type. n = 1,320 cells used to construct the embedding, 1,219 cells with t-type labels shown. Perplexity, 30. b, t-SNE embedding constructed using combined morphometric features and z-profiles. n = 636 cells. Perplexity, 30. c, t-SNE embedding constructed using combined electrophysiological and morphological features. n = 628 cells. Perplexity, 30. Ellipses show 80% coverage ellipses for the most prominent t-types (shaded) and for some groups of related t-types and some layer-restricted families (unshaded). We chose these groups to reduce the overlap between ellipses. d, Confusion matrices for classifying cells into seven transcriptomic families using kNN classifier (k = 10) and three feature sets. Each row shows what fraction of cells from a given family is classified in each of the seven families. The values in each row sum to 100% but only values above 5% are shown.