Metric Semantics by Workflow

Metrics are only meaningful when they match the way cells were detected.

Cell counting and classification

This workflow uses a shared Cell Detection marker such as DAPI or NeuN.

Valid metrics include:

• Total cells
• Marker-positive cells
• Marker-negative cells
• Marker-positive proportion
• Cells/mm2
• Marker-positive cells/mm2

Marker-positive means the detected cell expresses the selected marker. Marker-negative means the detected cell does not express the selected marker.

Independent detection

This workflow detects candidates directly on each marker staining.

Valid metrics include:

• marker-positive cells
• analyzed area
• marker-positive cells/mm2

A positive classification means accepted marker-positive cell. A negative classification means rejected candidate or false positive.

Invalid metrics for independent detection include:

• marker-positive proportion from a shared denominator
• negative cells as a biological population
• all-cell density

Choose metrics in the dashboard

Each dashboard plot has its own sources and metric settings. The available metrics follow the selected sources; changing the plot type does not create an all-cell denominator for independent detection.

In co-expression plots, the selected positive count represents the population chosen by the pair mode. In shared-detection workflows, proportions use the eligible shared cell population. In independent-detection workflows, use counts and densities for the selected marker-positive population.

Classifier selections are personal. Check the classifier for each staining before comparing the shared dashboard with a colleague or downloading a figure.

See Understanding statistics plots for plot choice and Using the statistics dashboard for configuration.

Practical rule

If a project has a shared cell detection source, marker-positive proportions can be meaningful.

If each marker detects its own cells independently, use marker-positive counts and densities instead.