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Define the comparison and what counts as a replicate
Before selecting controls, write down the outcome or spatial pattern you want to compare, how you define each group, and the population to which you intend to generalize. Then identify the study’s units. They may be different entities, and treating them as interchangeable can lead to invalid conclusions.
| Unit | What it means | Spatial omics example |
|---|---|---|
| Biological unit | The independent entity that supplies replication for a population-level comparison. | A donor or animal. |
| Experimental unit | The smallest entity independently assigned to a condition. | The entity assigned to treatment or control, as defined by the study design. |
| Observational unit | The location where a measurement is made. | A Visium spot, a high-resolution bin, or a segmented cell on an imaging platform. |
A tissue block may be assigned to a condition, while measurements within it are made at many spatial locations. Those locations can reveal spatial structure, but they do not become independent donors or animals. State which unit supports each part of the inference and report the number of independent biological samples.
Choose controls for the question they answer
A control is useful when it addresses a particular alternative explanation or checks a defined part of the assay. Biological controls and technical assay controls have different jobs; one does not automatically replace the other.
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| Control type | Question it can help address | Design consideration |
|---|---|---|
| Matched normal tissue | How does the disease tissue compare with an appropriate non-diseased tissue? | Specify what “matched” means for the cohort and tissue. Matching alone does not control slide or processing-batch differences. |
| Vehicle-treated material | What differs between the treatment and its vehicle? | Use it for a treatment contrast when the vehicle is the relevant comparator. |
| Assay positive or negative control | Did staining, hybridization, or another assay step behave as expected? | Define the assay step or signal the control is intended to check. |
| Reference sample carried across batches | Is a measured shift consistent with technical drift between runs? | Keep its technical-reference role distinct from the biological group comparison. |
| Control core in a tissue microarray | Can a common sample help assess staining or hybridization, normalization, or orientation? | Use it for the relevant quality-control purpose; it is not a substitute for biological replication. |
The National Cancer Institute Center for Cancer Research Collaborative Bioinformatics Resource lists input, IgG, vehicle-treated, and matched-normal controls in different experimental contexts. Treat these as examples, not a universal spatial-omics control panel: the right choice depends on the inference and assay step at issue.
Match, block, and randomize without confounding condition and run
Matching can improve comparability when a known factor is related to group assignment or the outcome. Candidate variables might include sex, collection time, tissue source, or processing, but include a variable because it matters to the study question or cohort—not simply because it is available. Record why each matching decision was made.
Blocking groups samples by a known source of variation; randomization distributes conditions across slides and processing batches where feasible. Use these approaches to avoid assigning all cases to one slide or run and all controls to another. Matching does not prevent a condition from becoming perfectly aligned with a technical run. If biology and batch are inseparable in the design, batch correction cannot reliably reconstruct the missing comparison.
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There is no universal rule in the cited guidance for paired versus unmatched designs. Choose pairing based on the intended comparison and the cohort, explain the rationale, and retain donor or matched-set identity for analysis where relevant.
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Different donors or animals are biological replicates for a group-level population comparison. Serial sections from one block, repeated processing of the same sample, and multiple spots or cells from one individual are not additional independent biological samples. Technical repeats can improve measurement precision for the sample they repeat, but do not increase the group’s biological sample size.
Analyses should account for the dependence among observations from the same individual. Treating that individual’s spots or cells as independent group-level replicates is pseudoreplication and can make uncertainty appear smaller than it is. Report the biological sample count separately from the number of sections, spots, bins, or cells measured.
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The NCI Center for Cancer Research Collaborative Bioinformatics Resource gives a general recommendation of at least three biological replicates per condition. That is institutional guidance, not a universal spatial-omics power calculation or a guarantee of adequate power. The appropriate sample size depends on expected variation, effect size, design, tissue heterogeneity, assay, and target population; plan it around the intended analysis and seek statistical input early.
Plan tissue coverage and quality around the spatial feature
Sample count alone does not determine whether a spatial study can detect a feature. The tissue area, region-of-interest (ROI) selection, and field-of-view coverage must capture the architecture and spatial scale relevant to the question. For imaging workflows, consider whether the fields of view cover the heterogeneity of interest within the available tissue. In-silico tissue simulations can help explore sampling needs, but they do not replace a design-specific power analysis.
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Check integrity and orientation before profiling. Histology can help identify necrosis, hemorrhage, or artifact-rich regions that could compromise interpretation. Quality indicators are assay-dependent: RNA integrity is central to sequencing-based workflows, while histological quality may be more informative for some imaging-based assays.
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For a tissue type that is new to the workflow, pilot the procedure. Use the pilot to assess section thickness and placement, tissue integrity, and whether the planned ROI captures the feature’s expected location. Set criteria for excluding damaged or unsuitable regions before comparing groups.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Design for batch assessment and preserve metadata
Record enough information to evaluate whether a group difference could instead reflect specimen handling, slide allocation, or assay quality. Collect metadata consistently and early, including:
- Biological identity, group, tissue, and specimen properties.
- Condition and, where applicable, treatment or vehicle assignment.
- Collection and processing variables, plus slide, run, and batch identifiers.
- ROI selection, assay details, and relevant quality indicators.
- Pairing or matched-set identity and the rationale for matching or blocking.
A 2026 benchmark by Zhao and colleagues in Genome Biology, published September 16, 2026, classifies spatial transcriptomics batch effects as inter-slice, inter-sample, cross-protocol or platform, and intra-slice. It finds that correction involves context-dependent trade-offs between removing batch effects and preserving biological structure; no method is universally optimal across the tissues, platforms, and batch scenarios studied. Prioritize a design that avoids confounding, then assess correction methods in light of the signal the study needs to preserve.
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Use a design check before committing samples
Before allocating scarce tissue, check that the plan connects the scientific question to the analysis:
- Write the estimand: specify the group contrast, target population, and spatial outcome or pattern.
- Name the units: identify the independently assigned experimental unit, the biological replicate, and the measurement unit.
- Assign each control a job: state the alternative explanation or assay step it addresses.
- Explain matching and blocking: list the variables and rationale, and preserve pairing identity if relevant.
- Map samples to slides and runs: distribute conditions across processing batches and randomize where feasible.
- Plan coverage and QC: define ROIs, tissue-quality checks, and how much area must be profiled to capture the feature.
- Plan analysis and sample size: account for within-individual dependence and justify the number of independent biological samples for the intended comparison.
- Capture metadata: ensure group, specimen, processing, run, ROI, and quality information will be retained for every sample.
The Bioconductor methods chapter and NCI Center for Cancer Research guidance support planning controls, replication, tissue quality, and metadata around the study question. The 2023 Nature Methods paper supports using in-silico spatial simulations to explore sampling requirements. None of these establishes a universal matching algorithm, control panel, or powered sample-size rule for every spatial transcriptomics or spatial proteomics study.
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