Awesome Expression Browser

screenshot of Awesome Expression Browser

A curated list of software and resources for exploring and visualizing (browsing) expression data

Overview:

The awesome-expression-browser is a curated list of software and resources designed for exploring and visualizing expression data. While the primary focus is on browsing expression data, the tools listed are versatile and can be used for various purposes. The foundation for this collection is credited to Sean Davis and his awesome-single-cell repository, pointing towards a reliable structure and valuable contributions within the field.

Features:

  • AMP RA: Publicly available RNA-seq and CyTOF for human synovial tissue from patients with rheumatoid arthritis (RA) or osteoarthritis (OA), visualized with Shiny.
  • Allen Brain Atlases: Provides access to Allen Brain Atlases and Data from the Allen Institute, useful for spatial transcriptomics and brain-related studies.
  • ASAP (Automated Single-cell Analysis Pipeline): Offers an automated pipeline for single-cell analysis, ensuring efficient processing and interpretation of data.
  • Brain Immune Atlas: A visualization tool for assessing various single-cell RNA sequencing datasets that capture the diversity of the brain's immune compartment.
  • Blood RNAexpress Atlas: Comprises separate browsers for mRNA and miRNA, allowing visualization of expression values across samples from the BLUEPRINT project.
  • Cellar: Facilitates interactive single-cell data analysis, enabling researchers to explore and analyze datasets effectively.

Summary:

The awesome-expression-browser is a valuable resource for researchers and professionals working with expression data exploration. With a wide variety of software and resources listed, users can benefit from tools like AMP RA for specific disease-related data visualization, ASAP for automated single-cell analysis, and Cellar for interactive data exploration. By leveraging these features, scientists can streamline their research processes and gain deeper insights into expression data across different datasets and projects.