The field of Spatially Resolved Transcriptomics (SRT) analysis is rapidly evolving, with increasing sample complexity, resolution, and tissue size. However, current software limitations hinder the integration of this intricate data. We introduce VR-Omics, a free, platform-agnostic tool that addresses this gap by providing automated processing, spatial data mining, and a user-friendly interface. Uniquely, VR-Omics facilitates multi-slice integration and 3D analysis, enabling whole-tissue exploration for the first time. Applied to pediatric cardiac rhabdomyomas, VR-Omics uncovered dysregulated metabolic networks previously undetectable through single-slice analysis, demonstrating its potential for novel biological discoveries. Furthermore, we aim to leverage the power of VR-Omics by investigating the development of Hypertrophic Cardiomyopathy in mouse hearts using a spatial transcriptomic approach. By building 4 spatio-temporal atlases containing multiple spatial whole transcriptome slides from an ALPK3 knockout line across two time points. Using VR-Omics we will create a comprehensive atlas highlighting the molecular differences in the early stages of the disease.