📖 Welcome to DepMap Gene Expression Explorer
Getting Started Guide
This interactive application allows you to explore gene expression data from the DepMap project. Follow this guide to learn how to use all the features.
🚀 Quick Start
1. Load Data
• Select a data version (24Q2 or 25Q2) from the sidebar
• Click Load Data button
• Wait for the data to load (progress bar will appear)
2. Set Up Analysis
• Enter gene names (e.g., PCDHB2, CD19) - one per line or comma-separated
• Optionally enter cell line names or ModelIDs to filter (you can use a mix of both)
• Or click Explore Cell Lines to browse and select cell lines by lineage (e.g., Lung, Breast). This opens an interactive panel where you can select lineages and their associated cell lines, allowing you to easily compare expression across tissue types without knowing specific cell line names.
• Select Primary Grouping (e.g., OncotreeLineage, OncotreeCode) - these are metadata columns from DepMap
• Optionally select Secondary Grouping for sub-categorization (eg. Sex, Age)
3. Run Analysis
• Click the Run Analysis button
• Visualizations will appear below!
Tip: Our app will automaticcaly check for spelling error. You can also mismatch cell line name and depmap_id in the cell line search box.
Use 'Reset to Defaults' if you get stuck
🔍 EXPLORE CELL LINES
Browse Cell Lines by Lineage
Use the Explore Cell Lines button in Analysis Controls to select one or more lineages (e.g., Lung, Breast) and their associated cell lines. Your choices auto-populate the Cell Line filter, helping you focus on specific lineages or subtypes.
How to Use:
Select tissue → choose cell lines (or check Select All at the bottom) → click Apply Cell Line Selection to add them to your analysis.
Features:
- Multi-select: Pick multiple lineages and cell lines
- Auto-populate: Selections sync with the filter
- Compare: Contrast expression across lineages
Tip: Use this when exploring expression patterns by lineage without needing to know individual cell line names.
⚠️ Warning System
Smart Alerts
The app will warn you when the selected metadata column has too many unique values, which may create crowded or slow visualizations.
✓ Proceed Anyway - Continue with current selection
✓ Choose Different Variable - Select a simpler option
📊 RNA Expression Heatmap
Parameters:
- Low/High Color: Choose gradient colors for expression levels
- Use Cartography Theme: Apply predefined color palette
- Cluster Rows: Group similar genes together
- Cluster Columns: Group similar samples together
- Sort by Secondary Group: Organize columns by secondary grouping
- Annotation Colors: Customize group annotation colors (when sorting)
- Scaling: Row (compare across samples), Column (compare across genes), or None
- Font Size: Adjust label size (8-20)
🧬 Protein Expression Heatmap
Only available when protein data exists for selected genes
Parameters:
- Low/High Color: Customize heatmap gradient
- Use Cartography Theme: Apply alternative color scheme
- Cluster Rows/Columns: Hierarchical clustering
- Sort by Secondary Group: Column organization
- Annotation Colors: Group color customization
- Scaling: Normalization method
- Font Size: Label visibility control
Data Source: Gonçalves et al. (2022), Pan-cancer proteomic map of 949 human cell lines. Cell Systems.
📈 Cell Line Explorer
View up to 3 features simultaneously: For example, facet by Gene × Sex, then color groups by Age
Parameters:
- Select Genes: Choose genes to visualize
- Max Genes to Display: Limit number of genes shown (1-12)
- Plot Font Size: Control text size (8-20)
- Primary Group By: X-axis grouping variable
- Color By: Add color dimension by categorical variable
- Plot Type: Boxplot, Violin, Bar, or Jitter
- Sort X-axis By: Name (A-Z) or Expression (High to Low)
- Facet By: Split into panels by Gene, categorical variable, or Gene × Variable
- Grid Title Font Size: Facet panel title size
- Grid Title Fill Color: Facet panel background color
🔬 RNA-Protein Correlation
Only available when protein data exists
Parameters:
- Select Gene: Choose gene for correlation analysis
- Color Points By: Add categorical color dimension
- Add Regression Line: Show linear fit with correlation coefficient
- Log Scale: Apply log2 transformation
- Point Transparency: Control point opacity (0.1-1.0)
- Plot Font Size: Adjust text size (8-20)
- Dot Size: Control point size (0.5-10)
Tip: Use this to assess RNA-protein correlation quality
🗂️ Data Explorer
Features:
- Search/Filter: Find specific entries in the table
- Show All Columns: Display all available columns
- Select Columns: Choose specific columns to view
- Select All / Deselect All: Quick column selection
- Click Headers: Sort by any column
- Download: Export filtered data as CSV, Excel, or TXT
Note: Column names with _RNA suffix indicate RNA expression, _protein suffix indicates protein expression
💾 Download Options
For Plots:
- Filename: Custom name for your file
- Width/Height: Dimensions in inches (4-30)
- Format: PDF (vector), PNG, JPEG, or TIFF (raster)
Tip: Use PDF for publications, PNG for presentations
💡 Pro Tips
Best Practices:
- Start Simple: Begin with 2-4 genes and basic grouping
- Check Warnings: Heed alerts about too many categories
- Use Faceting: Gene × Variable faceting compares genes across conditions
- Experiment with Sorting: Expression-based sorting reveals patterns
- Download Options: Adjust plot dimensions for better label visibility
- Protein Data: Not all genes have protein expression data
- Reset: Use 'Reset to Defaults' if you get stuck
Loading Data
Data Loading Status
Ready to Load DepMap Data
Please select a DepMap version from the sidebar and click 'Load Data' to begin.
- RNA Expression Data
- Cell Line Metadata
- Protein Expression Data
Data Loading Failed
Please try selecting a different version or check the data paths.
Analysis Controls
Cell Line Explorer by Lineage
Step 1: Select Lineages
Step 2: Select Cell Lines
Please select at least one lineage first.
Step 3: Apply Selection
RNA Expression Heatmap
Protein Expression Heatmap
Cell Line Explorer
RNA-Protein Expression Correlation
Data Explorer - Analysis Results (Wide Format)
💡 Quick Tips
• Use Search/Filter to find specific entries
• Select columns to customize your view
• Click column headers to sort data
• Download filtered results below