Dataset Explorer
Per-Study Expression & Immune Scores

Browse gene expression matrices, PCA plots, and immune scores by study.

Meta-analysis by Disease
Integrated Gene & Feature Results

Explore gene and immune feature meta-analysis results per disease.

Forest Plot Viewer
Study-Level Effect Sizes per Feature

View forest plots of effect sizes and confidence intervals across studies.

Cross-Disease Meta-analysis
Shared & Unique Disease Signatures

Compare dysregulated genes and features across diseases.

Immune Module Activity
Feature-based Module Scores

Explore modules of correlated immune features and their activity per study.

Correlation Analysis
Co-regulation of Immune Features

Explore correlations among immune features to uncover relationships.

  • Choose a study from the dropdown to load its gene expression matrix and preview the first few genes and samples.
  • Download the raw gene expression matrix and corresponding metadata.
  • Run PCA interactively on the expression data. Select a metadata field (e.g., disease, sex, treatment) to color the PCA plot and visualize sample clustering.
  • Explore features (e.g., pathway scores, cell type proportions) computed for the selected dataset. View the values in a table or as a heatmap.






  • Select a disease to view all available feature-level meta-analysis results (e.g., differentially expressed genes, enriched pathways).
  • Choose a specific feature type (e.g., genes, pathways, cell types) to display summary results in a table.
  • Adjust thresholds for effect size, p-value, and number of studies contributing to the analysis.
  • Generate an interactive volcano plot to visualize up- and down-regulated features and their statistical significance.
  • Use the dropdowns on the left to select a disease, feature type (e.g., genes), and a specific feature.
  • Click "Forest Plot" to generate a forest plot summarizing the standardized mean difference (SMD) for the selected feature across studies included in the meta-analysis.
  • Choose a feature (e.g., a specific gene, pathway, or cell type) to examine how it behaves across diseases.
  • View a summary table of its meta-analysis scores across diseases.
  • Generate a heatmap to visualize cross-disease patterns in regulation.

  • Select a study to view module scores — higher-order summaries of immune-related signals (e.g., transcriptional modules).
  • Display the module scores in a table or visualize them with a heatmap.
  • Download the module score matrix for external use.

  • Select two diseases to compare feature-level meta-analysis results.
  • Choose a feature type (e.g., genes, pathways, cell types) to base the comparison on.
  • Compute the correlation and view a scatterplot showing effect sizes across the two diseases.
  • Use this to identify shared or divergent patterns in feature regulation between conditions.

Learn About This App

This app was developed as part of a project focused on the computational analysis of autoimmune diseases. It integrates gene expression data, metadata, feature scores, and meta-analytical results from a curated collection of studies. The goal is to facilitate exploration, discovery, and hypothesis generation in autoimmunity research.


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