Table 1.
Comparison of ScRDAVis and other popular single cell data analysis tools.
Fig 1.
Overview of ScRDAVis workflow.
The schematic representation of the ScRDAVis workflow showcases its nine comprehensive modules. The workflow illustrates the sequential steps, inputs, outputs, and the interconnectivity of the modules.
Fig 2.
Single or multiple sample analysis.
A) Quality control (QC) metrics visualized as plots showing cell counts, gene counts, and mitochondrial percentages. B) Bar plot represents the cell count based on condition. C) Principal component analysis (PCA) plot for dimensionality reduction. D) UMAP/t-SNE visualizations of clusters representing cellular heterogeneity. E) Bar plot represents the cell counts for each cluster. F) UMAP/t-SNE plots after doublet detection and removal. G) Heatmap showing marker genes identified for each cluster. H) UMAP visualization with cell type annotations based on ScType, SingleR, GPTCelltype, or own annotation. I-K) Violin plot, feature plot, and ridge plot illustrating gene expression patterns across clusters. L-N) Condition-based differential expression analysis visualized using violin, ridge and volcano plots.
Fig 3.
A) Dot plot ranking enriched GO terms by p-value and gene ratio. B) Network plot illustrating relationships between enriched GO terms and shared genes. C) Bar plot highlighting the top 10 enriched GO terms. D) UpSet plot shows overlapping genes across multiple GO terms.
Fig 4.
Cell-Cell communication analysis.
A) Circular plot representing the overall signaling network with directed connections with count and B) with weight/strength. C) Interaction heatmap showing signaling intensity between cell types or clusters. D) Incoming and outgoing cell patterns for clusters. Visualizations of the MHC-II signaling pathway, including E) Circle, F) Chord, G) Bubble, and H) Hierarchy plots, along with I) Violin plots representing gene expression levels.
Fig 5.
Trajectory and pseudotime analysis.
A) Trajectory plot showing inferred developmental pathways with annotated branch points, roots, and leaves. B) Pseudotime-ordered tables linking cells to pseudotime scores. C) Bar plot visualizing pseudotime distributions. D) List of dynamically regulated genes identified along pseudotime.
Fig 6.
Co-Expression network analysis.
A) Soft power plot to determine the optimal soft power parameter for network construction. B) Module network plot with genes as nodes and edges representing correlations. C) Bar plot showing module relationships with experimental conditions. D) Feature plot mapping module-specific gene expression across UMAP embeddings. E) Correlation matrix between the modules and F) Bubble chart illustrating module expression. G) hub genes of modules 01, top 10 genes in the middle and the 15 in the outer circle. H) UMAP showing co-expressed modules with hub genes.
Fig 7.
Transcription factor regulatory network analysis.
A) Module network plots showing interactions between modules with positive and B) negative scores. C) Combined module network plot. D) UMAP-based module network plot with positive and negative scores. E) Feature plot for selected transcription factors. F) Bar plot highlighting TF contributions across gene modules. G) Positive and negative targets for the selected TF.