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Welcome to scLRSomatoDev documentation

This Shiny App has been made to visualize the ligand-receptor interactions between GABAergic and Glutamatergic cells during Somatosensory Cortex development.

Created and maintained by Rémi MATHIEU (INMED, INSERM, Aix Marseille Univ, France).

This documentation provides a guide to the features available in the app.

Getting Started

To begin using scLRSomatoDev, please visit the Installation page for setup instructions. You can choose from multiple installation options including an online version, local R setup, or Docker deployment.

Try a lite version of scLRSomatoDev Online!

Explore a lite, feature-focused version of our app directly in your browser—no installation required.

1. Go to sclrsomatodev.online and click the "Try the Lite version!" button.
2. Please be patient while the app loads. This process can take several minutes, and your browser may temporarily display an error message.
3. Once the "Overview" page appears, you're ready to start exploring!

[!NOTE] We are currently hosted on a server with limited resources. We appreciate your understanding as we work to improve performance.

Gene Expression

The "Gene Expression" tab allows you to visualize gene expression data in various ways:

• Metadata Table: Browse and download metadata for the 17 datasets.
• Clustering results and Feature plots: Visualize cell populations and gene expression on UMAP plots.
• Absolute Expression: View heatmaps or dot plots of gene expression per cell-type across development.
• Pseudo-maturation: Analyze gene expression dynamics along the pseudo-maturation axis.
• Pseudo-layer: Analyze gene expression dynamics along the pseudo-layer axis.
• Transcriptional landscape: Explore a 2D map of gene expression along both pseudo-maturation and pseudo-layer axes.

Ligand-Receptor Interactions

The "Ligand-Receptor" tab focuses on cell-cell communication:

• LRintercellNetworkDB: Explore the curated database of ligand-receptor pairs.
• LR Table: Access detailed tables of signaling analysis results.
• Number of interactions: Visualize the number of predicted interactions between cell types with a heatmap.
• Intercellular/Intracellular signaling: Investigate specific ligand-receptor pairs and their associated pathways with dot plots.