biojs-vis-inchlib

biojs-vis-inchlib visualizes biological datasets as cluster heatmaps to reveal sample- and feature-level patterns in gene expression, proteomics, metabolomics, and comparative genomics.

Key Features:

• Cluster Heatmap Generation: Generates cluster heatmaps that represent expression levels or presence/absence of features across multiple samples or conditions.
• Hierarchical Clustering: Organizes data into clusters using hierarchical clustering algorithms and similarity measures.
• Color Gradient Encoding: Encodes intensity or presence of specific biological features using color gradients in the heatmap.

Scientific Applications:

• Gene Expression Analysis: Visualizes gene expression data to identify clusters of genes with similar expression patterns across conditions.
• Proteomics and Metabolomics: Visualizes proteomic and metabolomic datasets to aid identification of biomarkers or pathway-level patterns.
• Comparative Genomics: Clusters genomic features to compare genetic variation across species or populations.
• Data Exploration: Summarizes large biological datasets visually to support exploratory analysis and hypothesis generation.
• Pattern Recognition: Highlights patterns and anomalies within complex biological data through clustered heatmap representation.

Methodology:

Hierarchical clustering algorithms organize data into clusters based on similarity measures, and resulting heatmaps use color gradients to represent intensity or presence of specific biological features.