Fig. 5: UniCell assists in rapid annotation of an integrated scRNA-seq Non-Small Cell Lung Cancer (NSCLC) dataset.

a Visualization demonstrating the basic steps underlying NSCLC sample collection, processing, and analysis using UniCell Deconvolve Base (UCDBase). b UMAP visualization of human lung cancer biopsy single cells, annotated by unsupervised leiden cluster (left) and sample of origin (right). c UMAP visualization of cell type labels applied for each leiden cluster using UCDBase deconvolution results to guide annotation. d UCDBase predictions are used to separate normal from malignant epithelium. UMAP visualization showing probability of malignant lung adenocarcinoma (LUAD) cells initially co-clustering with cells labeled as normal epithelium (left). Re-clustering select subpopulation reveals two major clusters separating by sample of origin, Adjacent Normal or Tumor (right upper). Visualizing UCDBase LUAD probabilities on re-clustered cells demonstrates Tumor-specific cluster contains the majority of predicted LUAD malignant cells. e UMAP visualization showing probabilities of four major lung normal epithelial cell types distributed across re-clustered cells. f Box plots of feature attribution weights (x-axis) for genes (y-axis) indicative of LUAD malignant cells learned by UCDBase. Sample size (n = 1576) reflects the total number of single cells annotated as malignant LUAD. For boxplots, center line, box limits and box whiskers correspond to the median, first and third quartiles, and the 1.5x interquartile range, respectively. Individual data points representing single cells are superimposed over each boxplot (Source Data File—Fig. 5f).