SkinOmicsExplorer

SkinOmicsExplorer is an interactive R Shiny portal for exploring and querying the multi-omics datasets generated in:

Yuqing et al.
A Spatially Coordinated Keratinocyte–Fibroblast Circuit Recruits MMP9⁺ Myeloid Cells to Drive IFN-I-Driven Inflammation in Photosensitive Autoimmunity
Yuqing Wang, Khashayar Afshari, Nazgol-Sadat Haddadi, Carolina Salomão Lopes, Chee-Huat Linus Eng, Nuria Martinez, Leah Whiteman, Ksenia S Anufrieva, Kevin Wei, Kirsten Frieda, Stefania Gallucci, Misha Rosenbach, Ruth Ann Vleugels, John E Harris, Mehdi Rashighi, Manuel Garber bioRxiv 2025.08.19.670635; doi: https://doi.org/10.1101/2025.08.19.670635

The portal integrates:

• single-cell RNA sequencing
• spatial transcriptomics (seqFISH)
• bulk RNA-seq
• targeted proteomics (OLINK & NULISA)

to enable interactive visualization of gene and protein expression across diseases, cell types, spatial contexts, and experimental perturbations and to visualize these in plots simiar to those used in our paper.

---

Key features

• Interactive scRNA-seq UMAP embeddings and gene expression plots
• Spatial visualization of seqFISH data across disease and UV perturbations
• Targeted proteomics visualization for in vivo and in vitro experiments
• Bulk RNA-seq perturbation analysis
• Publication-ready plot export (PNG/PDF)
• Multi-gene heatmap generation

---

Supporting repositories

SkinOmicsExplorer builds on custom visualization and analysis frameworks developed in:

• AddOns — reusable Shiny modules and visualization utilities
  https://github.com/garber-lab/AddOns

• scSpatial — spatial transcriptomics processing and visualization tools
  https://github.com/garber-lab/scSpatial

These repositories provide core functionality for interactive plotting, spatial rendering, and multi-omics integration.

---

Running

Docker images are available for easy running of the portal without installing dependencies

docker pull ghcr.io/garber-lab/skinomicsexplorer:latest

docker run -p 8888:8888 \
  -v "./shinyApp_content":/home/app_data/ \
  -it ghcr.io/garber-lab/skinomicsexplorer:latest

Then open your browser at:

http://localhost:8888

Resource requirements

For small and moderate datasets, default Docker settings may be sufficient. However, larger datasets (e.g., GSE179633 single-cell RNA-seq) require substantially more memory.

Common issue

At least on macOS (Docker Desktop), the default configuration limits available memory, which can cause:

• sudden app crashes
• no visible error messages
• instability when loading large datasets

This is typically due to out-of-memory (OOM) conditions.

Recommended Docker Desktop Settings (Tested)

To ensure stable performance with large datasets, we recommend:

• CPU: 12 cores
• Memory: 12 GB
• Swap: 2 GB

These settings were tested and found sufficient for interactive use with large single-cell datasets such as GSE179633.

Citation

Yuqing et al. A Spatially Coordinated Keratinocyte–Fibroblast Circuit Recruits MMP9+ Myeloid Cells to Drive IFN-I-Driven Inflammation in Photosensitive Autoimmunity. Nature Immunology (accepted)