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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchHandle batch effects, missing gene counts, blank coordinates, and physically absent tissue as separate problems. First identify what was measured and what was not; then audit the experiment and tissue, diagnose the source of variation, and choose correction, alignment, or imputation only when its assumptions fit. A corrected or predicted value is not a new observation, so preserve the raw data and label every derived result.
Identify what “missing” means before choosing a method
In spatial molecular data, “missing” can refer to several different things. The distinction determines what information is available and what any computational method can reasonably do.
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| Situation | What exists | What to do |
|---|---|---|
| A gene has a zero or absent count at a measured location | A measured coordinate and other expression data may exist; the gene may be undetected or genuinely low. | Assess capture quality and biological context before considering selective imputation. |
| A spatial coordinate is blank | The coordinate is known, but there may be no molecular measurement there. It could be outside tissue, a failed capture location, or another kind of gap. | Inspect its position and the image; do not treat it automatically as a zero-expression observation. |
| Tissue is damaged or absent in the image | There may be no intact tissue to measure at that location. | Document the physical gap. Any value inferred from neighbors or other data is a reconstruction, not a measurement. |
| A region falls outside the platform’s capture area | No measurement was collected there by that assay. | Keep it distinct from a failed spot or gene dropout; use external evidence only as an explicitly labeled prediction. |
| A section between sampled sections was never measured | Adjacent sections may be available, but the intervening section has no direct measurement. | Align or reconstruct only if the scientific question requires it, and report that correspondence is inferred. |
SPCS explicitly distinguishes missing genes from entirely blank spots and uses spatial position and neighboring spots when deciding whether to pad a blank location. Its rule requiring more than 50% nonblank spots in a predetermined neighborhood is specific to that method, not a general cutoff for deciding whether to fill a location (SPCS, 2022).
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Before integration or imputation, make a record of the variables that could explain technical differences. At minimum, retain sample and donor identifiers, tissue region, section, slide, run, protocol or platform, and processing date. Include relevant technical covariates available for the assay.
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- Plot counts and detected features by sample, section, and run rather than viewing only a pooled embedding.
- Inspect tissue images alongside spatial plots of quality metrics and expression.
- Check whether a biological group is tied to a particular batch. If, for example, all samples from one condition were processed in one batch and all samples from another condition in a second batch, the two effects may not be separately identifiable from the data.
Correction cannot reliably recover a distinction the study design never allowed the analysis to make. Treat confounding as a design limitation, not as a software setting that can be fixed after collection.
Use context-aware quality control
Low expression alone is not proof that a location is technically poor. Evaluate several measurements together and compare them with the image and the tissue’s expected anatomy.
- Counts and detected features: low library size or few expressed features can reflect poor capture, cell damage, missing mRNA, or low reaction efficiency. The Bioconductor OSTA quality-control chapter describes these as possible explanations, not a definitive diagnosis.
- Mitochondrial proportion: use it where it is meaningful for the assay and tissue; interpret it alongside other indicators rather than as a universal exclusion rule.
- Cell counts: for cell-based assays, check segmented-cell counts and their spatial distribution.
- Image and anatomy: compare metrics with tissue integrity, boundaries, and known structure before dropping locations or regions.
Artifacts are not limited to obviously low-quality spots. BLADE addresses border effects, tissue-edge effects, and batch-level location malfunctions. Its study examined 37 10x Visium samples of liver and adipose tissue from humans and mice (BLADE, 2025). The scope of that study does not establish that every edge or low-depth location is an artifact; it illustrates why visual inspection or a read-depth threshold alone can make inconsistent decisions or discard biological signal.
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Tell batch variation from real biology
Ask where the variation occurs and whether it follows technical or biological structure. SpaBEAT, a 2026 benchmark of ten spatial integration methods, distinguishes four batch-effect settings:
| Batch structure | Question to ask |
|---|---|
| Inter-slice | Do sections from the same specimen differ in ways associated with sectioning or processing? |
| Inter-sample | Do samples differ in a pattern associated with sample handling or processing rather than their biology? |
| Cross-protocol or platform | Are measurements being compared across different protocols or platforms? |
| Intra-slice | Is technical variation present within a single section? |
These categories are not interchangeable, and the right correction depends on the structure, tissue, platform, sample size, and downstream task. SpaBEAT reports context-dependent trade-offs between batch removal and preservation of biological structure, with no universally optimal method across the tested conditions (Zhao et al., 2026).
Do not judge correction only by whether samples mix in a low-dimensional embedding. Check whether known anatomical domains, cell populations, marker patterns, and spatial relationships remain interpretable. Stronger mixing can be a warning if it also erases meaningful biological differences.
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Choose correction by the problem you need to solve
Batch correction or integration aims to reduce technical differences among measured data. It does not create measurements for absent tissue, and it should not be used as a substitute for identifying missingness. Compare candidate methods against the actual batch structure and intended analysis rather than choosing one by reputation or a single visualization.
- Batch-effect removal: Do technical differences decrease across the relevant samples, sections, or platforms?
- Biological preservation: Do expected tissue domains, markers, and cell populations remain distinct where the evidence supports them?
- Task fit: Does the method address the specific setting—such as cross-platform integration—rather than an unrelated form of variation?
- Practical fit: Consider required inputs, reproducibility, and computational cost for the study at hand.
Where practical, compare more than one suitable approach and report the evaluation criteria. A benchmark result is evidence about tested settings, not a guarantee for a different tissue, platform, or experimental design.
Align sections only when spatial correspondence matters
When adjacent sections or serial slices need to be compared—for example, to study cross-section correspondence or build a three-dimensional representation—alignment may be appropriate. PASTE aligns sections using both molecular similarity and physical distance, then can stack pairwise alignments (PASTE).
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An alignment estimates correspondence between sections; it does not turn an unmeasured location into an observation. Check inferred matches against anatomy or histology, and describe uncertainty and limitations. Alignment may be unnecessary if the question can be answered within each section independently.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Impute gene-level dropouts selectively
Imputation is most defensible when a value is plausibly missing for technical reasons, the method’s assumptions fit the data, and the result can be checked. It is not a default remedy for every zero count: low expression may be real, and spatial smoothing can blur boundaries between distinct tissue regions.
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- Preserve the original measurements before applying any imputation.
- Record which values were predicted and which were directly measured; keep that distinction available in downstream analyses and figures.
- Where possible, test performance on measured entries deliberately held out from the input, or compare predictions with independent evidence.
- Repeat key analyses without imputed values and check whether the scientific conclusion changes.
- Report the method and its assumptions, and avoid describing estimates as recovered ground truth.
Treat absent tissue as a reconstruction problem
If tissue is physically missing, damaged, outside the capture area, or absent because a section was never sampled, there is no direct expression measurement for that region. Adjacent sections, histology, reference atlases, or generative models may support a prediction, but the result must be described as reconstructed or predicted rather than measured.
STITCH, described in a 2026 preprint, proposes an approach to reconstruct spatial gaps (STITCH). A preprint is emerging research, not evidence that reconstruction is established routine practice. If reconstruction is central to the analysis, state what inputs support it, how it was validated, and which output values are inferred.
Report provenance so readers can interpret the result
Keep raw data, corrected data, aligned coordinates, imputed expression, and reconstructed regions distinguishable. In methods and figures, say which transformations were applied and to which samples or locations. State the validation checks and the limitations of any inferred correspondence or predicted value. This lets readers assess whether a pattern comes from measurement, correction, alignment, or reconstruction.
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