Fig. 3: Relationship between CCDI and tumor malignancy at the single-cell level of LUAD using scRNA-seq datasets. | npj Precision Oncology

Fig. 3: Relationship between CCDI and tumor malignancy at the single-cell level of LUAD using scRNA-seq datasets.

From: Integrating bulk, single-cell, and spatial transcriptomics to identify and functionally validate novel targets to enhance immunotherapy in NSCLC

Fig. 3

a The t-distributed stochastic neighbor embedding (t-SNE) plot shows the main cell groups from Philip Bischoff’s LUAD scRNA-seq dataset (n = 12). b The Uniform Manifold Approximation and Projection (UMAP) plot of epithelial cells exhibits diploid tumor cells and aneuploid normal cells, as inferred from scRNA-seq data using the Copy Number Karyotyping of Tumors (CopyKAT) algorithm. c The UMAP plot illustrates the distinct subpopulations of malignant epithelial cells. d This heatmap visualizes the pathway activity scores for different malignant epithelial cell subpopulations, as determined by Gene Set Variation Analysis (GSVA). e Volcano plot of differentially expressed genes (DEGs) in malignant epithelial cell subpopulations. f Trajectory analysis of malignant cells inferred by slingshot, mapping the progression of cells through different states over time or differentiation. g Gene pseudotime expression map demonstrated the dynamic changes in CCDI gene expression during the malignant epithelial cell progression. Each plot tracks the expression profiles of a CCDI gene along the inferred cellular trajectory. The x-axis represents pseudotime or progression stages, while the y-axis denotes gene expression levels. Color gradients or line plots depict the shifts in gene expression, highlighting key regulatory changes that occur as malignant epithelial cells evolve. h, i The above analysis was validated in the GSE123902 cohort (LUAD, n = 17).

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