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Start with the biological comparison you want to make, the population it should represent, and the independent unit that supports the inference—usually a donor or animal. Then choose controls for a defined purpose, match or block on justified sources of variation, and distribute conditions across slides and processing batches. Spots, bins, cells, and repeat sections are measurements, not additional independent donors.
Define the comparison and the unit of inference
Write down the outcome or spatial pattern of interest, how cases and controls are defined, and the population to which you want the result to apply. That is the comparison your design needs to support.
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Keep three units distinct:
- Biological unit: the independent entity that supplies replication in a population-level comparison, typically a donor or animal.
- Experimental unit: the smallest entity independently assigned to a condition. Depending on the design, this could be a person, animal, or another independently treated entity.
- Observational unit: where the assay records measurements: for example, a Visium spot, a high-resolution bin, or a segmented cell in an imaging assay.
The observational unit does not replace the biological unit. State which level is assigned to a condition and which level supports the intended inference before selecting samples or analysis methods.
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How do I choose controls for a spatial omics study?
Choose a control because it addresses a particular alternative explanation or assay-quality question. A control that helps interpret a disease contrast may not be the right control for a treatment comparison or an assay check.
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| Control type | What it can help address | Example or qualification |
|---|---|---|
| Biological comparison control | Whether a disease- or condition-associated pattern differs from a relevant comparison group. | Matched normal tissue may be appropriate for some disease studies; what counts as a suitable match depends on the question and cohort. |
| Treatment control | Whether an observed difference is associated with treatment rather than the treatment procedure or vehicle. | Vehicle-treated material is one possible control in a treatment comparison. |
| Assay control or reference tissue | Whether staining, hybridization, normalization, or assay behavior is working as intended. | A reference sample carried through batches can help reveal technical drift. A tissue-microarray control core can support staining or hybridization quality checks, normalization, or orientation. |
The NCI Center for Cancer Research’s experimental-design guidance lists input, IgG, vehicle-treated, and matched-normal controls in different contexts. These are examples, not a standard panel to apply to every spatial assay. Keep biological comparison controls distinct from positive or negative assay controls: they answer different questions.
How should I match, block, and randomize cases?
Matching can improve comparability when a known factor is related to group assignment or the outcome. Possible factors include sex, collection time, tissue source, or processing history. Select variables based on the study question and cohort knowledge, and record why each was included rather than matching automatically on every available characteristic.
Blocking groups samples by a known source of variation; randomization helps distribute groups across slides and batches. These approaches complement each other. For example, matching cases and controls does not prevent confounding if all cases are processed in one run and all controls in another. Allocate each condition across slides and runs where feasible, rather than letting condition determine technical placement.
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There is no universal rule for paired versus unmatched designs. Choose the strategy that fits the intended comparison and available cohort, then retain donor or matched-set identity for analysis when relevant. Batch correction cannot reliably recover a biological contrast when condition and processing run are inseparable.
How many biological replicates do I need?
Count independent donors or animals as biological replicates for a group comparison. Serial sections from one block and repeated processing of the same specimen are technical replicates: they may improve precision for that specimen, but they do not increase the number of independent biological samples.
Likewise, treating many spots or cells from one donor as though they were independent donors is pseudoreplication. Account for the dependence among measurements from the same individual in the analysis, and report the actual number of biological samples alongside the number of sections, spots, bins, or cells.
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The NCI Center for Cancer Research gives a general recommendation of at least three biological replicates per condition. This is institutional guidance, not a universal power calculation for spatial omics. The number needed depends on expected variation, effect size, design, tissue heterogeneity, assay, and target population. Plan sample size around the intended analysis and seek statistical input early.
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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsHow do I plan spatial coverage and tissue quality?
Sample number alone does not determine whether a study can detect a spatial feature. Sampling must cover the relevant tissue architecture and the feature’s expected location and scale. For imaging, consider whether fields of view cover meaningful heterogeneity within the available area. In-silico tissue simulations can help explore sampling requirements, but do not replace a design-specific power analysis.
ROI selection, orientation, and tissue integrity can affect what gets profiled. Histology can help verify orientation and identify necrosis, hemorrhage, or artifact-rich regions that may compromise measurements. Quality indicators are assay-dependent: RNA integrity is central to sequencing-based workflows, while histological quality may be more informative for some imaging assays.
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For an unfamiliar tissue type, pilot the workflow. Use the pilot to optimize section thickness and placement, check tissue integrity, and identify regions to avoid before committing scarce specimens to the full study.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I avoid and assess batch effects?
Prevent avoidable confounding in the design: distribute conditions across processing runs and slides, and randomize allocation when feasible. Also preserve enough information to identify where technical variation could enter the study.
A 2026 benchmark by Zhao and colleagues, published in Genome Biology on September 16, 2026, classifies spatial transcriptomics batch effects as inter-slice, inter-sample, cross-protocol or platform, and intra-slice. It reports that correction performance depends on the tissue, platform, and batch scenario, with trade-offs between removing technical effects and preserving biological structure. No method was universally optimal across those settings. Assess correction in the context of the signal the study needs to retain rather than assuming one method will work for every dataset.
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What metadata should I record?
Capture design and quality information consistently from the start. NCI Center for Cancer Research guidance recommends early metadata collection and consistent sample annotations. At minimum, preserve:
- Biological identity, group, condition, and donor or matched-set identity.
- Tissue and specimen properties, collection details, and processing variables.
- Slide, run, and batch assignments.
- ROI selection, assay details, and relevant quality indicators.
These records allow you to evaluate whether group differences align with technical runs, account for the design in analysis, and explain how samples were allocated.
How to compare candidate designs
Before choosing among feasible designs, check whether each one supports the same population-level question. Compare them on the following dimensions:
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- The population and estimand the design can support.
- Whether each control addresses the specific biological alternative or assay step of interest.
- The number of independent biological samples and the dependence of measurements within each sample.
- How well conditions are balanced across slides, runs, and processing batches.
- Whether tissue quality and spatial coverage capture the feature being studied.
- Feasibility given tissue availability and study resources.
Platform choice may trade spatial resolution, gene coverage, and input requirements, but should follow the biological question and specimen constraints rather than dictate the control logic. No universal matching scheme, control set, or sample-size target applies across spatial transcriptomics and spatial proteomics studies.
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