Understanding statistics plots
Choose a plot and metric for a specific biological question. In the dashboard, each plot has its own sources, grouping and filters, so you can keep complementary views side by side.
For editing instructions, see Using the statistics dashboard.
Choose sources and metrics first
A plot can include individual stainings and compatible co-expression pairs. Check the selected classifiers before interpreting the comparison.
Shared cell detection supports a common detected-cell population, allowing total counts, marker-positive counts, marker-positive proportions and density metrics. Independent detection supports accepted marker-positive counts, analyzed area and marker-positive cells/mm².
For independent detection, a negative classification is a rejected candidate, not a biological marker-negative cell. Marker-positive proportions and all-cell density are unavailable without a shared denominator. See Metric semantics by workflow.
Match the plot to the question
Heatmaps
Use a heatmap to scan patterns across animals and brain regions. It is useful for checking consistency and identifying possible outliers.
Read the color legend. A missing or gray measurement is unavailable, not zero. Different color ranges can make similar values look different.
Bar plots
Use horizontal or vertical bars to compare group means across regions. Individual markers show animal values when enabled.
The thin variability line shows one sample standard deviation above the mean when at least two animal values are available. It is not a standard error or confidence interval.
Keep a dot/strip or box/violin plot beside the bars to inspect the animal-level values behind each mean.
Dot/strip plots
Use dot/strip plots to see individual animal values. Check group overlap, consistency and whether one animal drives an apparent difference.
Box/violin plots
Use box/violin plots to inspect medians, spread and distribution shape alongside individual animal values. Be cautious about interpreting distribution shape with small groups.
Effect-size plots
Choose a baseline group to compare standardized group differences across regions. Check the animal values and available sample sizes before interpreting a large effect.
An effect size describes the magnitude of a comparison. It does not by itself establish statistical significance.
Scatter plots
Choose X and Y metrics to examine relationships within the same animal-region measurements. Look for clusters and outliers, and use metrics with a meaningful biological or technical relationship.
Mirrored bars
In the Bar plot editor, choose the mirrored layout to compare two metrics side by side for the same groups and regions. Read each side using its own metric, units, axis and range.
Anatomical plots
Anatomical plots map regional group means onto a selected atlas section. A region can be colored only where it is visible at the chosen coordinate and has a usable value.
Use these plots to locate regional patterns anatomically. They do not show animal-to-animal variability on their own. Atlas-space plots require a registered project.
Region-free cells and density
Region-free plots show positively classified cells from a staining or co-expression directly in atlas space, independently of selected brain regions.
Cells shows sampled cell locations. The number of dots may be limited by the sampling setting, so displayed dots are not an exact count of the underlying population.
Density summarizes the spatial pattern in patches. Patch size, image coverage and color ranges affect its appearance. Separate group panels help compare patterns; group-comparison mode highlights relative differences between two selected groups.
Choose a target atlas coordinate and Slice range. For AP views, the range extends on both sides of the target coordinate. Registered sections intersecting that volume contribute, including tilted sections. Cells and image coverage are clipped to the same volume, so a tilted section may contribute only part of its imaged area.
Density is expressed as cells per projected mm². Within each animal, counts and covered areas are combined across contributing sections. Animal densities are then averaged with equal weight. These are not volumetric cell densities.
Use both region-free modes for qualitative spatial exploration. Dot counts and patch colors do not establish precise regional measurements, measured group effects or statistical significance. Detail only projects cannot provide these atlas-space views.
Make comparisons readable
- Apply the intended animal, group, condition and sex filters to each plot. Regional plots also support brain-region filters.
- Choose grouping based on the study design. Group, condition and sex options depend on the project setup.
- Compare equivalent metrics on consistent scales and ranges. A shared-range option applies within the plot; check separately configured dashboard plots individually.
- Use positive bounds for manual logarithmic-axis ranges.
- Read legends and units before comparing colors or bar lengths.
Separate hemisphere measurements retain their left/right labels where available. Joint regions remain combined, matching Results.
Check surprises against Results
Before interpreting a strong pattern, confirm the classifiers, data freshness, selected subset and metric meaning. Inspect unexpected animals or regions in Results.
After registration changes, including AP coordinate or tilt changes, reprocess outdated measurements and atlas points before relying on the plots.
For custom statistical tests or calculations, export the project for analysis with NeuroQP Python.