Color Mapping

Core Feature

This feature is available in all MATISSE Explorer deployments.

Color mapping assigns colors to data points based on annotation values, allowing you to visualize patterns and distributions in your spatial transcriptomics data.

Selecting a Color Basis

From the Annotation List

1. Open the Explorer extension
2. In the Sidebar, browse the Annotations list
3. Click on an annotation name to apply it as the color mapping
4. The map updates immediately with the new coloring

Annotation Type Tabs

Switch between annotation types to find the coloring you need:

Tab	Use For
Categorical	Cell types, clusters, discrete groups
Continuous	Gene expression, numeric scores

Categorical Color Mapping

When you select a categorical annotation, each category receives a distinct color.

Legend

The legend displays:

• Category names
• Assigned colors
• Optionally, point counts per category

Interacting with Categories

• Hover over legend items to highlight that category on the map

Continuous Color Mapping

When you select a continuous annotation, values are mapped to a color gradient.

Color Gradient

The default gradient maps:

• Low values → One end of the color scale
• High values → Other end of the color scale

Legend

The legend displays:

• Color gradient bar
• Minimum value
• Maximum value
• Current range settings

Adjusting Color Range

For continuous annotations, you can adjust the min/max range to emphasize specific value ranges.

Why Adjust Range?

• Highlight differences - Narrow the range to see subtle variations
• Handle outliers - Exclude extreme values that compress the color scale
• Focus on relevant values - Set range to biologically meaningful thresholds

Cutoff Adjustment

Use the cutoff slider to exclude low values from the color mapping:

• Adjust the cutoff percentage (0-50%) to filter out low-expressing cells
• Useful for highlighting cells with significant expression
• Applied before the color scale transformation

Colormap Selection

MATISSE Explorer provides separate colormaps for continuous and categorical annotations.

Global Colormap Settings

The default colormaps are configured in Settings (accessible via the gear icon in the Activity Bar). Changes to the global colormap affect all visualizations throughout the application.

See Preferences for detailed configuration options.

Available Colormaps

Continuous Colormaps

Sequential - Best for expression data:

Colormap	Best For
Cool	General expression (default)
Viridis	Perceptually uniform
Inferno	High contrast, dark theme
Plasma	Vivid color range
Magma	Subtle gradients
Cividis	Color blindness friendly
Turbo	High contrast rainbow
Blues/Greens/Reds/Purples	Single-hue emphasis

Diverging - Best for data with a center point:

Colormap	Best For
Red-Blue	Fold change visualization
Red-Yellow-Green	Three-way comparison
Red-Yellow-Blue	Temperature-like scale
Brown-Blue-Green	Earth tones
Pink-Yellow-Green	Soft diverging
Spectral	Multi-color diverging

Categorical Colormaps

Colormap	Best For
Rainbow	Many categories (default)
Sinebow	Smooth color transitions
Turbo	High contrast distinct colors

Choosing a Colormap

Consider:

• Annotation type - Use continuous colormaps for numeric values, categorical for discrete groups
• Accessibility - Viridis and Cividis work better for color blindness
• Contrast - Dark background may need different colors than light
• Consistency - Use global settings for uniform appearance across views

Variable Set Coloring

Color by computed variable set scores instead of individual annotations.

How to Use

1. Create a variable set with genes of interest

2. Select the variable set for coloring

3. Choose a combination method:

   Method	Use Case
   Max	Highlight cells expressing any gene highly
   Min	Find cells expressing all genes
   Sum	Total expression across gene set
   Avg	Average expression level
   UMI Count	Normalized expression

4. The map colors by the computed score

Scale Types

For continuous data, different scale types affect how values map to colors.

Scale	Description	Use When
Linear	Direct mapping	Most cases (default)
Log	Natural logarithm	Wide value ranges (requires min ≥ 0)
Log2	Base-2 logarithm	Wide value ranges (requires min ≥ 0)
Log1p	log(x+1) transformation	Single-cell RNA-seq data
SymLog	Symmetric log	Data with zero or negative values
Sqrt	Square root mapping	Moderate compression
Power	Squared mapping	Emphasize small values

Changing Scale Type

1. Look for the scale selector in color settings
2. Choose the appropriate scale
3. The coloring updates to reflect the new scale

TIP

Log and Log2 scales are disabled when the minimum value is negative. Use SymLog or Log1p for data that includes zero or negative values.

Tips for Effective Coloring

For Cell Type Analysis

1. Use categorical coloring with cell type annotations
2. Check that colors are distinguishable
3. Use legend hover to verify identifications

For Gene Expression

1. Start with linear scale
2. Adjust range to focus on expressing cells
3. Try log scale if expression varies widely
4. Compare multiple genes by switching quickly

For Finding Patterns

1. Try different colormaps to see patterns
2. Adjust range to maximize contrast
3. Combine with spatial/embedding views
4. Use filters to reduce noise

Troubleshooting

All Points Same Color

• Check if annotation has variation
• Adjust the color range
• Verify data is loaded correctly

Colors Too Similar

• Try a different colormap
• Narrow the color range
• Use log scale for compressed ranges

Legend Not Showing

• Ensure an annotation is selected
• Check if legend is collapsed or hidden
• Resize the workspace if needed

Related Topics

• Annotations - Browse available annotations
• Variable Sets - Create gene groups
• Layer Settings - Adjust point appearance
• Image Adjustments - Modify background image