Fig. 3: GSDensity enables pathway centric analysis of tumor scRNA-seq data.
From: Pathway centric analysis for single-cell RNA-seq and spatial transcriptomics data with GSDensity
UMAP visualization of the TNBC-1 data using different hallmark gene sets (a: G2M checkpoint; b: mTORC1 signaling; c. glycolysis; d: mitotic spindle) to classify cells. Cells which are the most relevant to the hallmark are labeled as ‘positive’. The UMAP was calculated using RNA expression (transcriptome) with default parameters using Seurat. e Violin plot visualization of the proliferation marker, MKI67, in TNBC-1 data, with the cells classified by G2M checkpoint gene set. Wilcoxon test (two-sided) was applied here. Source data are provided as a Source Data file for panels e–h. f UMAP visualization of the TNBC-1 scRNA-seq dataset. The cells are colored based on the clustering information from the inferred CNV profile. g The Silhouette scores and number of clusters out of choices of the ‘resolution’ parameter to cluster tumor cells in TNBC1 dataset with Seurat. The x-axis is the scanned ‘resolution’ (0.1 to 1.2) in Seurat ‘FindClusters’, and y-axis shows the Silhouette score. The text label (numbers) shows the number of clusters out of such parameter. h The expression level of GAS6 and TYRO3 genes in immune cells, G2M checkpoint positive, and G2M checkpoint negative tumor cells from the TNBC-1 data. Wilcoxon test (two-sided) was applied here.
