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. 2021 Mar 25;16:19. doi: 10.1186/s13024-021-00443-6

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© The Author(s) 2021

Open AccessThis 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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Fig. 3 — scRNAseq reveals transcriptional skewing in hCD33m microglia. a Unsupervised and iterative machine-learning based clustering using SCCAF of 13,485 cells in the merged analysis of control, hCD33m and hCD33m datasets from Experiment 1. A total of 12 clusters were identified. b Bar graphs showing the absolute number of cells from each isoform present in each cluster (top) and their respective proportions (bottom). c UMAP projection of the individual Control, hCD33M, and hCD33m datasets. d Heatmap demonstrating the cluster based differential expression of key stratifying genes. e Violin plots of genes from cluster 0. Expression levels in hCD33m⁺ microglia are significantly higher (P < 0.05) than the other two other genotypes for each gene