i2d_R

i2d_R transforms images into simulated quantitative datasets via digital convolution to enable extraction and analysis of complex quantitative information and downstream graph-based clustering.

Key Features:

• Image-to-Data Simulation: Uses digital convolution to convert images into simulated quantitative datasets suitable for analysis.
• Minimum Spanning Tree (Prim's Algorithm): Implements Prim's algorithm to organize data points into a minimum spanning tree that minimizes total edge weight.
• Community Detection (Modularity Optimization): Applies modularity optimization to identify clusters (communities) that maximize network modularity.
• Backbone Branch Identification of MSTs: Isolates significant branches within minimum spanning trees to highlight important network backbones.

Scientific Applications:

• Biomedical Research: Enables dissection of intricate gene networks into sub-clusters with similar biological functions using simulated datasets derived from images.
• Data Analysis Enhancement: Converts visual imaging data into analyzable formats to facilitate quantitative analysis of complex biological structures.

Methodology:

Applies digital convolution to transform images into simulated datasets and performs graph clustering using Prim's minimum spanning tree, modularity optimization for community detection, and backbone branch identification on MSTs.