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. 2023 Nov 30;55(12):2104–2116. doi: 10.1038/s41588-023-01572-y

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© The Author(s) 2023

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. 8 — a. UMAP visualization showing OB neurons 1-11 and OB neurons 1-17 identified from EasySci-RNA (top) and EasySci-ATAC (bottom), colored by subcluster id (left), aggregated gene expression or gene activity of OB neurons 1-11 gene markers (middle) and OB neurons 1-17 gene markers (right). b. UMAP visualization showing oligodendrocytes-6 and oligodendrocytes-7 identified from EasySci-RNA (top) and EasySci-ATAC (bottom), colored by subcluster id (left), aggregated gene expression or gene activity of oligodendrocytes-6 gene markers (middle) and oligodendrocytes-7 markers (right). c. UMAP visualization showing microglia-9 identified from EasySci-RNA (top) and EasySci-ATAC (bottom), colored by subcluster id (left), aggregated gene expression or gene activity of microglia-9 gene markers (right). Subcluster marker genes were identified by differential expression analysis using scRNA-seq data (Methods). d. Heatmap showing the gene expression (top) and the promoter accessibility (bottom) of microglia-9 enriched genes across subclusters. The EasySci-RNA data (UMI count matrix) and EasySci-ATAC data (read count matrix) were aggregated per subcluster, normalized by the total number of reads, column centered, and scaled. Of note, rare subclusters from RNA-seq data that were not detected in ATAC-seq data were not included in this analysis. e-f. Heatmap showing the results of LDSC analysis of the SNPs associated with the indicated phenotypes in DEpeaks across main cell types (e) or across microglia subtypes (f), colored by z score of regression coefficient. *FDR < 0.05, **FDR < 0.01, ***FDR < 0.001. Only cell types that contain DEpeaks from all autosomes are included in this analysis.