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Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →A well-powered spatial molecular study starts with the biological contrast and the spatial feature you intend to test—not a generic target number of spots, cells, or slides. For conclusions meant to generalize across people or animals, independent donors or animals usually determine biological replication. Use relevant pilot or reference data to simulate the planned endpoint, sampling design, and multiple-testing threshold before committing to a cohort.
How many samples do you need?
There is no universal minimum cohort size for a spatial omics case–control study. The required number depends on the tissue, platform, endpoint, effect worth detecting, between-unit variability, spatial sampling plan, and planned significance or false-discovery-rate threshold. A sample count from a published method paper is not a substitute for a power analysis matched to your study.
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For example, the 2026 VIMA study analyzed datasets with 27, 42, and 75 samples. Those are dataset sizes, not recommended minimums: Reshef et al. explicitly state that they did not perform a statistical analysis to choose sample sizes.
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Do more cells or fields replace more patients?
No. Separate the biological unit, experimental unit, and observational unit before calculating sample size. In a study intended to generalize across patients or animals, independent donors or animals are typically the biological replicates. Fields of view (FOVs), sections, slides, spots, bins, and segmented cells are measurements nested within those units; counting them as independent biological replicates creates pseudoreplication.
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Additional measurements within a specimen can improve its characterization or precision, but they do not provide the same population-level information as additional independent donors or animals. The balance between more biological units and deeper sampling within each specimen should reflect the primary endpoint, tissue heterogeneity, and available material.
Define the endpoint before planning power
“Spatial difference” can mean several distinct outcomes. A study powered to detect differential expression in a prespecified region of interest (ROI) is not automatically powered to find local disease-associated patches or test a global difference in spatial organization. Define the primary endpoint, the population and contrast, and which analyses are confirmatory versus exploratory.
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| Primary question | Design and analysis implications |
|---|---|
| Is a spatial pattern globally associated with case or control status? | Specify a global sample-level spatial-pattern endpoint and plan the corresponding case–control test and multiplicity control. |
| Where are local disease-associated tissue neighborhoods? | Plan for local feature discovery, including the number of features tested and the correction used for multiple testing. |
| Does expression differ between groups within a defined ROI? | Use an endpoint-specific differential-expression power workflow, with the ROI, slice replicates, effect sizes, and adjusted-p-value threshold specified. |
| Does a cell type, adjacency, or another spatial feature differ? | Define that feature precisely and use a power approach that represents its measurement and analysis; power for another endpoint does not transfer automatically. |
Estimate power with data that resemble the study
A useful calculation needs a minimum effect that would matter biologically, expected within-group variation, case–control allocation, the planned threshold for significance or false discovery, and the exact endpoint and analysis. Estimate these inputs from pilot measurements, prior data from the same tissue and platform, or a defensible reference dataset. State where those inputs are uncertain.
- Specify the estimand. Write down the biological population, case–control contrast, and spatial outcome. Make clear what result would count as a meaningful effect.
- Set the independent replication count. Identify which donors or animals are independent biological units and distinguish them from repeated observations within a specimen.
- Represent the planned hierarchy. Simulate or resample at the biological-unit level and include the planned sections, regions, fields, or other spatial sampling where relevant.
- Use the intended analysis and correction. Match the calculation to the primary endpoint, covariates, and significance or FDR threshold; a calculation using a different test may misstate power.
- Compare feasible designs. Vary the number of independent units and spatial sampling choices, then examine how conclusions change under plausible effects and variability.
- Report assumptions and limits. Document the source of effect and variance inputs, the simulated design, and conditions under which the model may not represent the actual tissue or assay.
Choose spatial coverage around tissue architecture
Decide which anatomical region and structure matter before selecting FOV geometry. Field size, number, and placement should capture the expected heterogeneity and the spatial scale of the feature. More fields do not invariably increase power: poorly placed fields can miss the structure of interest, no matter how many spots or cells they contain.
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In-silico tissue generation can help compare FOV size, count, placement, and spatial resolution, provided the simulated tissue plausibly represents the study tissue. Its results are exploratory and depend on tissue-structure assumptions and available data. Consider section depth, tissue quality, ROI selection, and platform resolution alongside nominal coverage.
When using tissue microarrays
Tissue microarrays can process cores from many patients on one slide and reduce within-slide technical variation. Their tradeoff is sampling bias: small cores may fail to capture tissue heterogeneity. Core dimensions and spacing also need to fit instrument capture limits and imaging capacity.
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Prevent technical factors from masquerading as disease effects
Randomize cases and controls across slides, processing batches, and runs where feasible. Avoid placing all cases in one batch and all controls in another, because disease status would then be difficult to separate from technical effects. Collect relevant sample-level demographic and technical covariates; if the analysis adjusts for them, retain enough case–control overlap and independent units to distinguish covariate effects from disease status.
Choose a power workflow that matches the question
VIMA for case–control spatial-pattern association
VIMA (variational inference-based microniche analysis) is one option for testing spatial molecular patterns. It learns patch representations with an ensemble of conditional variational autoencoders, forms potentially overlapping microniches, summarizes microniche abundance per sample, and tests global and local associations. Its framework uses permutations for significance and can report associated patches, effect directions, and FDR control. The authors applied it to rheumatoid arthritis immunofluorescence, ulcerative colitis CODEX, and dementia MERFISH datasets and reported type-I-error calibration in simulations. These results support considering VIMA for relevant spatial-pattern questions; they do not establish it as best for every technology or endpoint.
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PoweREST for spatial-transcriptomics differential expression
PoweREST estimates power for differential-expression detection using bootstrap resampling of spots within ROIs, adjusted p-values, and varying slice-replicate counts and effect sizes. The described workflow is Visium-oriented and is specific to its differential-expression setting. It assumes, among other things, that power within an ROI is not determined by the spatial configuration destroyed during bootstrap resampling. Use it only when that and the method’s other data and endpoint assumptions fit the intended study.
In-silico tissue generation for sampling choices
An in-silico tissue framework can explore how tissue structure, feature size, FOV count and placement, and spatial resolution affect detectability. It helps compare coverage plans rather than supplying a universal sample-size answer. Its usefulness depends on whether the simulated structure resembles the tissue and biological feature under study.
Compare candidate designs before collecting the cohort
| Design axis | Question to resolve |
|---|---|
| Biological replication | How many independent donors or animals are included in each group? |
| Effect and variation | What minimum relevant effect and within-group variability are supported by pilot or reference data? |
| Endpoint and multiplicity | Is the primary test global, local, differential-expression, cell-type, or adjacency based, and what correction is planned? |
| Spatial sampling | Will FOV size, count, and placement cover the relevant structures and tissue heterogeneity? |
| Resolution and coverage | Can the platform resolve the spatial scale required by the biological question? |
| Confounding | Are both groups represented across batches, slides, runs, and relevant covariates? |
| Tissue availability | Do section depth, core size, tissue quality, or ROI selection limit representation? |
| Method assumptions | Does the simulation or resampling approach reflect the intended tissue, platform, endpoint, and analysis? |
The priority among these axes depends on the endpoint and constraints. A defensible design makes the biological replication, sampling plan, and power assumptions explicit rather than treating a large measurement count as proof of adequate power.
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