GraphBio

GraphBio provides visualization and analysis methods for omics datasets to facilitate exploration and interpretation of high-dimensional biological data.

Key Features:

• Heatmap: For displaying complex data matrices and patterns across samples or features.
• Volcano Plots: To identify significantly changed features between two conditions.
• MA Plots: For visualizing differences in expression levels between conditions.
• Network Plots: To illustrate relationships and interactions among entities.
• Dot Plots: For representing individual data points and categorical distributions.
• Chord Plots: For displaying inter-relationships in circular layouts.
• Pie Plots: To show proportions of categories within a dataset.
• Four Quadrant Diagrams: For visualizing bivariate data distributions across four sectors.
• Venn Diagrams: For illustrating overlaps between different datasets or groups.
• Cumulative Distribution Curves: To depict cumulative frequencies and distributional differences.
• Principal Component Analysis (PCA): To reduce dimensionality and visualize major sources of variation.
• Survival Analysis: For analyzing time-to-event (survival) data.
• Receiver Operating Characteristic (ROC) Analysis: To assess the performance of binary classification models.
• Correlation Analysis: For identifying pairwise relationships between variables.
• Text Cluster Analysis: For grouping similar text-based data into clusters.

Scientific Applications:

• Differential expression analysis: Identification of significantly changed features between conditions using volcano and MA plots.
• Expression pattern visualization: Exploration of sample- and feature-level patterns using heatmaps and dot plots.
• Network and interaction analysis: Visualization of relationships and interactions among biological entities.
• Dimensionality reduction and variation exploration: Use of PCA to summarize and visualize major sources of variation.
• Time-to-event analysis: Analysis of survival and other time-to-event outcomes using survival analysis.
• Classifier performance evaluation: Assessment of binary classifiers using ROC analysis.
• Correlation and association assessment: Detection of relationships between continuous or ordinal variables via correlation analysis.
• Overlap and set analysis: Comparison of dataset intersections and overlaps using Venn diagrams.
• Distributional assessment: Examination of cumulative frequencies and distributional differences using cumulative distribution curves and four-quadrant diagrams.
• Text data clustering: Grouping and exploration of similar text elements through text cluster analysis.

Methodology:

Implements visualization and statistical analysis methods including heatmaps, volcano plots, MA plots, network plots, dot plots, chord plots, pie plots, four-quadrant diagrams, Venn diagrams, cumulative distribution curves, PCA, survival analysis, ROC analysis, correlation analysis, and text cluster analysis.