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. 2023 Apr 6;55(5):807–819. doi: 10.1038/s41588-023-01355-5

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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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Fig. 7 — a, Leiden clustering of single-cell RNA-seq data from NSCLC⁵¹ colored by cluster ID (upper) or cell-type label (lower). Exploration of tumor markers within the cancer-specific clusters enabled further resolution into NSCLC subtypes, including recapitulation of the dedifferentiated TI-1 subtype identified earlier from bulk RNA-seq data (Cluster 12; Extended Data Fig. 8a). b, Association between cell types identified in NSCLC single-cell data and selected genes and metagenes from the wound healing (C1) and immune activation/exhaustion (C2) feature clusters in the SU2C-MARK cohort or with previously described relationships to immunotherapy response^35,45–50. Features within larger correlation blocks in bulk RNA-seq data did not always arise from the same single-cell sources (for example, TGF-β versus macrophages/monocytes in the wound healing cluster, and dedifferentiated TI-1 versus immune-activated M-2 in the immune activation/exhaustion cluster).