There is no universal sample-size target for a case–control study of spatial molecular data. A defensible design starts with the biological question and primary spatial endpoint, counts independent donors or animals as the biological replicates, and uses pilot or comparable data to simulate the planned analysis and tissue-sampling strategy. More spots, cells, or fields of view cannot compensate for a design that misses the relevant tissue structure or confounds disease status with processing batch.
How many samples do you need?
The number depends on the tissue, platform, endpoint, effect worth detecting, between-unit variability, case–control allocation, spatial sampling plan, and planned significance or false-discovery threshold. Set these assumptions before choosing a cohort size; a generic rule of thumb cannot account for them.
Count independent biological units
For a study intended to generalize across patients or animals, independent donors or animals usually determine the biological sample size. Sections, slides, fields of view (FOVs), spots, bins, and segmented cells collected from the same donor are nested measurements, not additional independent biological replicates. Treating them as independent creates pseudoreplication. Additional measurements within a specimen can improve precision for that specimen, but do not replace additional independent units.
Separate the units in your design
- Biological unit: the patient or animal to which population-level inference is intended to apply.
- Experimental unit: the entity independently assigned to a group or treatment.
- Observational unit: where measurements are collected, such as a section, FOV, ROI, spot, bin, or cell.
Write down these units and their nesting before calculating power. This makes clear which observations contribute independent replication and which contribute within-specimen detail.
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Do not use published dataset sizes as a minimum
Reshef et al. reported VIMA analyses on datasets of 27, 42, and 75 samples in a 2026 Nature Methods study. Those are counts for the datasets analyzed, not recommended cohort sizes: the authors explicitly state, “We did not perform a statistical analysis for choosing sample sizes.”
Which spatial endpoint are you powering?
Power is tied to the exact question and test. Define a primary endpoint and the boundary between confirmatory and exploratory analyses; a design powered for one endpoint is not automatically powered for another.
Global spatial-pattern association
This asks whether overall spatial organization or a prespecified spatial summary differs with case–control status. The analysis and power plan should reflect the sample-level quantity being compared and the intended test.
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Local feature or neighborhood discovery
This asks where case–control-associated patches or tissue neighborhoods occur. The analysis may test many local features, so the multiple-testing procedure and planned threshold belong in the power calculation.
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Differential expression within defined regions
This asks whether expression differs between groups within specified ROIs. ROI-level differential-expression power does not establish power to detect global spatial-pattern changes or discover local associated patches.
Other spatial endpoints
Cell-type detection, adjacency, and other spatial features require their own endpoint definitions and analysis assumptions. The more possible spatial features a study can test—and the harder their structure is to parameterize—the more important it is to define the primary target in advance.
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How to estimate power for the planned study
- State the estimand. Specify the population, case–control contrast, biological outcome, and primary spatial endpoint. For example, define the difference in a prespecified spatial feature between cases and controls rather than saying only that the study will examine spatial organization.
- Choose a minimum relevant effect. Decide what difference would matter biologically, not just what difference could be detected. Support the value with pilot measurements, prior data from the same tissue and platform, or a defensible reference dataset.
- Estimate variation and allocation. Use relevant data to estimate within-group variability and account for the planned numbers of cases and controls. Record the significance or FDR threshold that matches the intended analysis.
- Represent the sampling hierarchy. Simulate or resample at the biological-unit level and include the planned within-unit spatial sampling. Reflect the intended numbers and placement of sections, fields, or ROIs instead of treating every measured spot or cell as an independent replicate.
- Compare feasible designs. Vary the numbers of independent units and the spatial sampling plan, then evaluate the endpoint-matched power under the same assumptions and multiplicity procedure.
- Report assumptions and limits. State what data informed effects and variability, how the simulated tissue and sampling relate to the study, and where those assumptions may fail.
Use a method that matches the endpoint
An in-silico-tissue framework can explore how tissue structure, feature size, FOV count, FOV size and placement, and spatial resolution affect detectability. Its results depend on available data and whether the simulated tissue represents the study tissue plausibly.
PoweREST estimates power for spatial-transcriptomics differential-expression studies using bootstrap resampling of spots within ROIs, adjusted p-values, and modeling across slice replicates. Its described use is Visium-oriented. The approach assumes, among other things, that power within an ROI is not determined by spatial configuration destroyed by bootstrap resampling; use it only if that and its other data and endpoint assumptions fit the intended study.
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Spatial coverage is part of the design, not just a technical setting. Define the anatomical region and the size of the structure or event of interest first, then choose FOV geometry, number, and placement to capture the expected heterogeneity. A high count of measured spots does not help if the fields miss the feature being studied.
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Plan placement around anatomy and heterogeneity
- Specify the tissue region and spatial scale relevant to the biological question.
- Determine whether a single field can contain the structure of interest or whether several fields are needed to represent heterogeneity.
- Choose locations using a consistent, prespecified strategy rather than allowing group status to determine what gets sampled.
- Compare alternative FOV sizes, counts, and placements through in-silico tissue generation when the model adequately reflects known tissue structure.
More fields do not always mean more power: their value depends on where they are placed, the feature’s spatial scale, and whether they add coverage or merely repeat information from the same specimen.
Consider tissue microarrays carefully
Tissue microarrays can increase throughput by processing many patient cores on one slide and may reduce within-slide technical variation. Small cores can also miss tissue heterogeneity. Match core dimensions and spacing to the instrument’s capture limits and available imaging capacity, and consider whether the sampled cores represent the regions required by the endpoint.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do you prevent technical confounding?
Keep case–control status distinguishable from technical factors by distributing groups across processing conditions wherever feasible. If all cases are processed in one batch and all controls in another, an observed group difference cannot be cleanly separated from a batch difference.
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- Randomize cases and controls across slides, processing batches, and runs.
- Measure relevant sample-level demographic and technical covariates that could affect the signal.
- Preserve overlap between groups across batches and covariates so their effects are distinguishable from disease status.
- Include the planned covariate handling in power simulations when the analysis adjusts for those variables.
VIMA’s framework accepts sample-level covariates such as age and sex and describes control for demographic and technical confounders. Covariate adjustment does not remove the need for overlap or independent units across groups.
Which analysis and power methods fit the question?
| Method or framework | Endpoint it addresses | What it does | Scope and limitation |
|---|---|---|---|
| VIMA | Case–control association with spatial molecular patterns, including global and local associations. | Learns patch representations with an ensemble of conditional variational autoencoders, forms potentially overlapping microniches, summarizes microniche abundance per sample, and uses permutation-based tests. It can return associated patches, effect directions, and FDR-controlled results. | Evaluated across rheumatoid arthritis immunofluorescence, ulcerative colitis CODEX, and dementia MERFISH datasets, with type-I-error calibration reported in simulations. This supports it as a method option, not as the best choice for every technology or endpoint. |
| In-silico tissue generation and power analysis | Exploring how tissue structure and spatial sampling choices affect detectability. | Can compare feature sizes, FOV numbers, sizes and placements, and spatial resolutions in simulated tissue. | Exploratory; usefulness depends on the availability of suitable data and how well the simulated tissue resembles the study tissue. |
| PoweREST | Power estimation for spatial-transcriptomics differential expression in ROIs. | Uses bootstrap-resampled ROI spots, adjusted p-values, and a modeled power surface across slice-replicate counts and effect sizes. | Its described approach is Visium-oriented and relies on assumptions about resampling and ROI power. It is not a general method for global spatial-pattern discovery. |
Choose the analysis before finalizing the power plan: its endpoint, sample-level structure, and multiplicity correction determine what the simulation needs to represent.
What should you compare before committing to a design?
| Design axis | Question to answer |
|---|---|
| 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 | Do FOV size, count, and placement capture the tissue heterogeneity and structures of interest? |
| Resolution and coverage | Does 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? |
| Assumptions | Does the simulation or resampling method reflect the intended tissue, platform, endpoint, and analysis? |
The right balance among these factors depends on the endpoint and constraints. Compare alternatives using the same explicit assumptions rather than treating one design feature—such as more fields or finer resolution—as a substitute for the rest.
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