ClustVis

ClustVis performs principal component analysis and heatmap generation to visualize and enable exploration of patterns, trends, and outliers in high-dimensional biological datasets.

Key Features:

• Data input: Accepts tabular data provided as delimited text files or pasted tabular data compatible with common spreadsheet programs.
• Principal Component Analysis (PCA): Performs PCA for dimensionality reduction and projects major variance components onto two-dimensional scatterplots.
• Heatmap generation: Produces heatmaps with clustering to display expression or measurement matrices across samples or features.
• Clustering and annotations: Supports adjustable clustering options and incorporation of experimental annotations or groupings for sample or feature labels.
• Visualization customization: Provides parameter controls for clustering choices and annotation display to tailor analytical visual outputs.

Scientific Applications:

• Genomics: Visualizing gene expression or other high-dimensional genomic matrices to identify sample groupings, patterns, and outliers.
• Proteomics: Visualizing proteomic abundance or feature-level matrices to detect trends and cluster-related patterns.
• Exploratory dimensionality reduction: Facilitating exploratory analysis and hypothesis generation through PCA and clustered heatmaps across diverse high-dimensional biological datasets.

Methodology:

Principal Component Analysis (PCA) for dimensionality reduction with projection onto two-dimensional scatterplots; heatmap generation with clustering capabilities and annotation of experimental conditions or groupings.