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Summed Abundance Calculation ​

Introduction ​

The Summed Abundance Calculation aggregates the abundance values of all features (e.g., genes or proteins) across each sample. This provides a high-level overview of total expression and is primarily used as a quality control and exploratory analysis step.

Summed abundance helps identify global differences between samples or groups, detect technical biases, and assess whether normalization or filtering is required before downstream analyses.

Accessing the Tool ​

  1. Open a study in the Explorer
  2. In the left-hand navigation, expand Expression View
  3. Select Summed Abundance Calculation
  4. Click Start your expression analysis

Step 1 – Select Expression Data ​

  1. Select your files
    Choose one or more expression datasets (e.g., abundance or expression matrices). The summed abundance will be calculated across all selected features for each sample.

  2. Select from My Lists (optional)
    If you want to calculate summed abundance for a specific subset of features, select a list from My Lists.

  3. Click Continue.

Step 2 – Choose Contrasts, Samples, and Filters ​

Choose Your Contrasts ​

  • Select a contrast variable (e.g., sample_group, condition, treatment).
  • This determines how samples are grouped and displayed in the results.

Samples to Include ​

  • Select which samples to include in the calculation.
  • Use Select All to include all available samples.
  • Selected samples appear as tags in the selection field.

Expression Filters ​

Optional filters can be applied to improve data quality:

  • Valid value
    Requires each feature to have valid expression values in at least a specified percentage of samples (default: 70%).

  • Valid value in at least one group
    Requires each feature to have valid values in at least one sample group.

These filters help remove features with excessive missing values.

Feature Metadata (Optional) ​

  • If feature metadata files are available, you can select:
    • A metadata file
    • Specific feature categories
  • This allows summed abundance to be calculated for subsets of features (e.g., pathways or functional groups).

Click Process to start the calculation.

Step 3 – Choose Your Plots ​

While the data is processed, select which visualizations to generate:

  • Bar Chart
    Displays summed abundance values per group or sample.
  • Box Plot
    Shows the distribution of summed abundance across groups.

Click Continue once processing is complete.

Results ​

Table View ​

The Table tab displays summed abundance values with one row per sample.

Typical columns include:

  • Sample
  • Contrast / group
  • Summed abundance

The table supports:

  • Searching
  • Column sorting
  • Pagination
  • Downloading results as a file

Bar Chart View ​

The Bar Chart visualizes summed abundance across sample groups.

Features include:

  • Hover tooltips showing exact values
  • Zoom and pan controls
  • Download plot as an image (PNG/SVG)

This view is useful for quickly comparing total abundance across conditions.

Focus Panel ​

The Focus panel on the right allows you to:

  • Change the expression matrix
  • Switch contrast variables
  • Reset the analysis

Changes update the visualization without rerunning the entire workflow.

Interpretation Guidelines ​

  • Large differences in summed abundance may indicate:
    • Batch effects
    • Technical bias
    • Inconsistent normalization
  • Similar summed abundance across samples suggests comparable overall signal levels.
  • Summed abundance does not indicate differential expression of individual features.

Best Practices ​

  • Use summed abundance as a quality control checkpoint
  • Apply expression filters to reduce noise from missing values
  • Investigate strong group-level differences before running PCA or statistical tests
  • Combine with PCA and Relative Expression analyses for deeper insight

Summary ​

The Summed Abundance Calculation provides a fast, intuitive way to assess global expression patterns across samples. By aggregating expression values and visualizing them across groups, it helps identify technical artifacts and guides informed decisions for downstream analyses.