PlaqView

PlaqView 2.0 provides an integrated platform for exploration and analysis of cardiovascular single-cell RNA sequencing (scRNA-seq) datasets to enable interrogation of cellular composition and molecular mechanisms.

Key Features:

• Expanded Dataset Repository: Hosts a large, centralized collection of cardiovascular single-cell datasets including human aortic aneurysm samples, gene-specific mouse knockouts, and healthy reference datasets derived from scRNA-seq studies.
• Gene Query: Enables searching and analysis of specific genes across included scRNA-seq datasets.
• Metadata Browser: Provides examination of dataset metadata to contextualize samples and experimental conditions.
• Cell Identity Prediction: Applies computational methods to predict cell types from gene expression profiles.
• Ad Hoc RNA-Trajectory Analysis: Performs RNA-trajectory analyses to investigate dynamic cellular processes and developmental trajectories.
• Drug-Gene Interaction Prediction: Identifies potential interactions between drugs and genes to support therapeutic research.
• High-Performance Computing: Integrates high-performance computing resources to perform complex analyses on large-scale datasets.
• Systematic Data Sharing Strategies: Provides a centralized, organized approach to dataset exploration and re-analysis to address limited systematic data sharing.

Scientific Applications:

• Atherosclerosis and Cardiovascular Disease Research: Facilitates single-cell investigation of tissues and lesions relevant to atherosclerosis and other cardiovascular diseases.
• Disease Mechanism Elucidation: Enables molecular-level interrogation of cellular populations to study mechanisms underlying cardiovascular pathologies.
• Therapeutic Target Identification: Supports identification of candidate drug targets through analysis of gene expression and predicted drug-gene interactions.
• Comparative Studies: Allows comparison of cellular responses across conditions such as disease versus healthy controls and across genetic perturbations (e.g., gene-specific mouse knockouts).

Methodology:

Uses a backend data processing framework for efficient handling and analysis of large-scale scRNA-seq datasets with emphasis on reproducibility and scalability.