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There is no universal number of samples, cells, spots, or fields of view that guarantees adequate power for a spatial molecular study. Start by defining the primary biological endpoint and the smallest effect worth detecting, then estimate the number of independent biological units needed using pilot data or simulations that reflect your tissue, sampling plan, and planned analysis. More cells or larger sampled areas can improve measurement and spatial coverage, but they do not replace independent donors or animals when the goal is to generalize across them.
Why spatial studies do not have one sample-size formula
Power is the probability that a study will detect an effect of a specified size under stated assumptions. A sample-size estimate therefore depends on what counts as an effect, how variable that effect is between biological units, and how the data will be analyzed. Spatial studies add further dependencies: tissue architecture, the size and frequency of the feature of interest, spatial dependence, and where and how much tissue is sampled.
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Different questions require different calculations. Detecting a rare cell type, testing whether two cell types are unusually adjacent, comparing tissue organization between cohorts, and finding differentially expressed genes (DEGs) are not interchangeable power problems. A number calculated for one endpoint or platform should not be carried over to another without justification.
Before asking “How many samples per group?”, specify the organism or patient population, tissue and disease context, assay, primary contrast, and statistical model. If these are not yet known, the responsible answer is a planning workflow—not a guessed sample count.
#1 Best Overall
First decide what the study must be powered to detect
Name one primary endpoint
Write the main outcome in measurable terms. For example, the endpoint might be a difference in expression of a prespecified gene set, detection of a particular cell type, enriched adjacency between two cell types, or a change in tissue organization between cohorts. Identify the primary comparison as well: for example, treated versus control or disease versus reference.
Set the minimum meaningful effect
Specify the smallest difference that would matter biologically, not just a difference that might reach statistical significance. For expression, this could be a minimum condition-associated change under a defined analysis. For an adjacency or cell-detection question, define the event and the size of change that would alter the biological interpretation. The endpoint and effect definition must match the planned analysis.
Choose the error and testing plan
State the desired power and type-I error threshold for the primary test, and account for multiple testing where the analysis examines many genes, cell pairs, or other features. If several endpoints or contrasts are important, decide in advance which one drives the sample-size calculation and how the others will be handled. The calculation is only as interpretable as these choices.
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Count independent biological units—not just measured objects
For a comparison intended to generalize across people or animals, donors or animals are generally the relevant independent biological units. The Bioconductor OSTA design guidance distinguishes three levels:
- Biological unit: the entity to which the scientific conclusion is intended to generalize, such as a human donor or mouse.
- Experimental unit: the smallest unit independently assigned to a condition.
- Observational unit: where a measurement is made, such as a spot, bin, segmented cell, or field of view.
In Visium, Visium HD/Stereo-seq, and CosMx/Xenium examples discussed in that guidance, measurements may be made at the spot, bin, or segmented-cell level while the animal or donor is the experimental unit for a condition comparison. Treating thousands or millions of cells from a small number of donors as thousands or millions of independent biological replicates risks pseudoreplication: the analysis can mistake many correlated observations from the same specimen for many independent samples.
Keep technical repeats separate from biological replication. Multiple cells or spots in a slice, serial sections from one block, or repeated runs or slides for one specimen can add measurement precision or tissue coverage. They do not, by themselves, add independent donors or animals to a group comparison. Where feasible, randomize conditions across slides and processing batches so that batch is not confounded with condition.
Rank #3
Build the estimate from data or simulations that match the endpoint
- Assemble relevant inputs. Use pilot or appropriately matched public data to estimate between-unit variability, expression and detection properties, feature frequency, spatial structure, and plausible effect sizes. Match the tissue, assay, and biological context as closely as possible.
- Represent the intended sampling plan. Specify the number of biological units per group and the number, size, and placement of sections, regions of interest (ROIs), fields of view (FOVs), or other measurements within each unit. Include the planned spatial coverage and resolution.
- Simulate the planned analysis. Generate or resample data under plausible assumptions, then run the same preprocessing, endpoint definition, statistical model, and relevant multiple-testing procedure that the study will use. Estimate how often the analysis detects the prespecified meaningful effect.
- Compare feasible designs. Repeat the calculation for alternative numbers of biological units and alternative within-unit sampling plans. Examine whether the conclusion changes when plausible effect sizes, variability, tissue heterogeneity, or spatial organization differ.
- Document uncertainty. If the pilot is too small to characterize tissue structure or between-unit variability, report a sensitivity range across assumptions rather than presenting a single estimate as precise.
Conventional power calculations rely on a target error rate, an effect size, and sample size. For spatial data, coordinates and tissue organization can also shape detection, so a calculation based only on an average expression difference may not answer a question about adjacency or organization. A simulation is useful only to the extent that its generated tissue and analysis are plausible for the planned study.
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Available approaches have different scopes. They are complementary, not interchangeable calculators.
| Approach | Endpoint and platform scope | Data and assumptions | Practical qualification |
|---|---|---|---|
| Study-specific pilot-based simulation or resampling | Can be adapted to a defined endpoint and analysis, including spatial questions, if the data-generating assumptions are appropriate. | Uses relevant preliminary or public data to represent variability, spatial features, effects, and the intended sampling design. | Its output is conditional on the representativeness of the data and the plausibility of the model; test alternative assumptions when these are uncertain. |
| PoweREST | A published framework for differential-expression power in Visium spatial transcriptomics. | Uses nonparametric bootstrap replicates within ROIs and factors including spatial expression, condition-associated log-fold changes, gene-detection rates, and slice replicates. It is described for use when preliminary spatial data are available; an interactive application based on two cancer datasets is also described for settings without such data. | Do not treat it as a general calculator for every spatial platform or endpoint. The method and its assumptions should be checked against the planned study. |
| spaCraft | A repository-described planning approach for multi-sample spatial transcriptomics, with spatially adjusted differential-expression and compositional endpoints. | Learns a cohort-level generative model from pilot samples and uses generate-recover-test Monte Carlo simulations, rediscovering spatial domains in each replicate. The repository reports validation on 10x Visium, Visium HD, and Stereo-seq. | The README specifies R 4.1.0 or later and a C++ toolchain. The repository says its methods manuscript is in preparation; confirm current documentation, version, and suitability before relying on it. |
| In-silico tissue framework | Illustrates power questions involving cell-type detection, enriched cell-cell adjacency, and tissue or cohort organization. | Simulates tissue structure and sampling; the approach highlights how difficult spatial organization can be to parameterize, particularly for cohort-level questions. | Interpret results as conditional on the simulated tissue model and the sampled data, not as universal thresholds. |
When evaluating any method, check whether it represents the biological and experimental units correctly, captures the tissue geometry relevant to the endpoint, uses pilot data where appropriate, and repeats the intended analysis inside its simulations. Also check sensitivity to assumptions about heterogeneity and effect size, as well as software requirements and availability.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Plan spatial coverage as well as sample count
For imaging-based assays, a large number of observations from poorly chosen locations may still miss the biological structure the study is meant to measure. Define the expected feature scale—such as a tumor region, brain layer, or tertiary lymphoid structure—and plan ROI or FOV size and placement to capture relevant tissue regions. Consider whether a fixed or constrained imaging area limits coverage. Tissue microarrays can increase cohort throughput, but small cores may miss within-tissue heterogeneity.
A 2023 in-silico tissue study provides a study-specific illustration: in its spleen setup, sampling more than 7.5% of the assayed tissue area—approximately 123 × 123 μm, or about 5,600 cells—was estimated to recover a particular CD4+ and CD8+ T-cell adjacency as significant with 80% probability. This is not a recommended FOV size or power threshold for other tissues. The reported inflection point reflected the spatial scale of organization in that simulated example.
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Best Value
Report the design so the estimate can be interpreted
A useful protocol or paper should let readers see what was counted as a replicate, what effect the study was designed to detect, and how spatial sampling entered the calculation. Report:
- Biological units per group, the experimental or randomization unit, and the observational units.
- Sections, slides, ROIs or FOVs, and measurement units per biological unit, including their size, placement, and spatial coverage.
- The tissue feature scale and the primary endpoint, contrast, and minimum meaningful effect.
- The expected variability and effect assumptions, target power and type-I error, and treatment of multiple testing.
- The data source or simulation model, exact analysis procedure used in simulations, and handling of batch effects.
- Sensitivity to plausible alternative assumptions, and whether the estimate is pilot-based or an assumption-based scenario.
Without the organism or patient population, tissue context, endpoint, effect size, between-unit variability, platform, sampling plan, and statistical model, a numerical sample-size recommendation would imply certainty the available information cannot support.
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