🔬 Single-cell RNA-seq Data Analysis Pipeline

Explore multi-species scRNA-seq datasets, perform differential expression analysis, and visualize cellular heterogeneity. Follow the step-by-step workflow below:

Step 1: Load and Visualize Your Dataset

Select an organism and dataset to begin your analysis. View UMAP projections and explore cellular composition.



📥 Download Markers (CSV)
Step 2: Select and Analyze Cell Clusters

Choose specific cell clusters for detailed analysis. Visualize gene expression patterns and co-expression relationships.







📥 Download Correlation Results (CSV)
Step 3: Compare Gene Expression Between Conditions

Perform differential gene expression analysis between clusters or groups. Generate volcano plots and identify key biomarkers.

📥 Download DGE Results (CSV)


📥 Download Enrichment Results (CSV)
Step 4: Aggregate Single Cells for Bulk-like Analysis

Convert single-cell data to pseudo-bulk samples for robust differential expression analysis using DESeq2.

📥 Download Pseudo-bulk Results (CSV)


📥 Download Pseudo-bulk Enrichment (CSV)

MASLDatlas Documentation

Complete guide to using the Multi-species scRNA-seq Atlas of MASLD

Table of Contents

1. Overview

MASLDatlas is an interactive web application for exploring and analyzing single-cell RNA sequencing (scRNA-seq) data from multiple species affected by Metabolic dysfunction-Associated Steatotic Liver Disease (MASLD).

Key Capabilities:

  • Interactive visualization of scRNA-seq datasets (UMAP, Violin plots, Heatmaps)
  • Differential gene expression analysis
  • Gene correlation and co-expression studies
  • Functional enrichment analysis (GO, KEGG, Reactome, WikiPathways)
  • Pseudo-bulk DESeq2 analysis
  • CSV export functionality for all results

Supported Species:

  • Human
  • Mouse
  • Zebrafish
  • Integrated datasets

2. Getting Started

Step 1: Select a Dataset

Navigate to the Import Dataset tab and:

  1. Choose an organism from the dropdown menu
  2. Select a dataset from the available options
  3. Wait for the data to load (progress bar will appear)

Step 2: Explore the Data

Once loaded, you can:

  • Visualize cell types on UMAP plots
  • Filter data by specific cell types or conditions
  • View gene expression patterns
Tip: Start with a smaller dataset if you're new to the tool to familiarize yourself with the interface.

3. Analysis Workflow

Step 1: Import Dataset

Select your organism and dataset of interest.

Step 2: Visualize Gene Expression

  • UMAP plots: Visualize cell populations
  • Violin plots: Compare gene expression across cell types
  • Gene filters: Search and select genes of interest

Step 3: Gene Correlation Analysis

  • Select two genes to analyze their co-expression
  • Choose statistical test (Spearman or Pearson)
  • View scatter plots with correlation coefficients
  • Download results as CSV

Step 4: Differential Gene Expression (DGE)

  • Define two groups to compare
  • Choose statistical method (Wilcoxon, t-test, etc.)
  • View ranked gene lists with statistics
  • Filter by log fold-change, p-value, or score
  • Export results to CSV

Step 5: Enrichment Analysis

  • Select genes from DGE results
  • Choose enrichment database (GO, KEGG, Reactome, etc.)
  • Visualize enriched pathways and terms
  • Download enrichment tables

Step 6: Pseudo-bulk Analysis

  • Aggregate cells by sample
  • Run DESeq2 for robust differential expression
  • View volcano plots and results tables
  • Perform enrichment on pseudo-bulk results

4. Key Features

Interactive Visualizations
  • UMAP/t-SNE projections
  • Violin and box plots
  • Heatmaps and dot plots
  • Volcano plots for DGE
  • Customizable color schemes
Statistical Analysis
  • Wilcoxon rank-sum test
  • Student's t-test
  • Spearman/Pearson correlation
  • DESeq2 for pseudo-bulk
  • Multiple testing correction
Enrichment Databases
  • Gene Ontology (GO)
  • Biological Processes (BP)
  • KEGG Pathways
  • Reactome
  • WikiPathways
Advanced Options
  • Cell type filtering
  • Gene set enrichment analysis
  • Custom gene lists
  • Batch effect visualization
  • Quality control metrics

5. Exporting Results

All analysis results can be exported as CSV files for further analysis in Excel, R, Python, or other tools.

Available Exports:

  • Cell Type Markers: Download marker genes for selected clusters
  • Correlation Results: Export gene-gene correlation data
  • DGE Results: Save differential expression statistics
  • Enrichment Analysis: Download pathway enrichment tables
  • Pseudo-bulk Results: Export DESeq2 results
  • Pseudo-bulk Enrichment: Save enrichment for pseudo-bulk data

How to Export:

  1. Complete your analysis in the respective tab
  2. Look for the Download ... (CSV) button below each results table
  3. Click the button to download the file
  4. Files are named with the analysis type and current date
Pro Tip: Export your results regularly to keep track of different analyses and comparisons.

6. Troubleshooting

Common Issues and Solutions:

Dataset not loading
  • Check your internet connection
  • Try refreshing the page
  • Select a different dataset to verify the issue
  • Clear browser cache if problem persists
Export button returns error 500
  • Ensure you have completed the analysis before exporting
  • Check that the results table contains data
  • Try re-running the analysis
  • If error persists, the data may be too large - try filtering first
Enrichment analysis not working
  • Verify that genes are selected in the DGE table
  • Check that the organism matches your dataset
  • Ensure the fenr package is installed
  • Try with a different enrichment database
Plots not displaying
  • Wait for analysis to complete (check for spinner)
  • Ensure required inputs are selected
  • Try zooming in/out in your browser
  • Check browser console for JavaScript errors

Need More Help?

If you encounter issues not covered here:

  • Check the GitHub repository for known issues
  • Open an issue with a detailed description of the problem
  • Include screenshots and error messages when possible

Citation

If you use MASLDatlas in your research, please cite our publication:

MASLDatlas: Multi-species single-cell RNA-seq atlas of Metabolic dysfunction-Associated Steatotic Liver Disease. [Journal Name], [Year].

MASLDatlas v1.0 | GitHub Repository | Last updated: October 2025