Use bulk RNA sequencing (RNA-seq) as an orthogonal check on aggregate expression patterns—not as proof that a spatial assay has correctly located transcripts or assigned them to cells. Aggregate spatial counts into a pseudo-bulk profile that matches the biological question, compare shared genes against a tissue- and context-relevant bulk reference, and report both rank-based concordance and gene-level deviations.
What bulk RNA-seq can—and cannot—validate
Bulk RNA-seq measures an aggregate tissue sample. Compared with a spatial profile aggregated over the same tissue or a defined region, it can test whether the two measurements show similar expression patterns across genes. A high correlation supports similar aggregate expression ranking under that comparison.
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Bulk data cannot, on their own, verify where a transcript is located, whether a spatial assay assigned it to the correct cell, or whether the two methods measured the same absolute transcript abundance. Those claims need evidence suited to localization, cell assignment, or abundance—not just a correlation coefficient. The distinction matters because spatial assay sensitivity and cell segmentation affect how spatial measurements should be interpreted; see the 2025 reproducibility assessment in Nature Biotechnology.
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Build a comparison that answers a specific question
Define the claim
Decide what you want the comparison to support before selecting a metric. It might assess broad expression patterns, relative abundance across genes, reproducibility between samples, or consistency with a biological interpretation. Bulk RNA-seq is most directly informative about aggregate expression; it does not resolve a spatial claim.
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Match the tissue and comparison unit
Use matched specimens when available. If the spatial sections are being compared with a bulk cohort or a public reference, match tissue type and biological context as closely as possible, and describe the result as a cohort-level comparison rather than same-specimen validation. Published benchmarks have compared spatial tissue microarrays with bulk references from TCGA or GTEx, but those references do not make unmatched samples equivalent.
Choose the spatial aggregation level to fit the comparison: whole-tissue pseudo-bulk for a whole-tissue bulk profile, or a defined region of interest when the question concerns that region. Comparing one cell or a small region directly with whole-tissue bulk risks a compositional mismatch; if you do it, make that mismatch explicit.
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Harmonize genes and expression values
Map gene identifiers consistently, then restrict the comparison to genes measured in both modalities. State how many shared genes were included. Also document each modality’s quantification and normalization so readers can interpret the resulting values.
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There is no universal normalization prescription in the cited examples. One 2025 benchmark compared spatial expression normalized to 100,000 with average bulk FPKM in a particular figure; that is a study-specific analysis choice, not a general rule. The benchmark describes comparisons over shared or overlapping genes. See the 2025 Nature Communications study.
Quantify agreement and inspect where it breaks down
Report a rank-based statistic such as Spearman correlation across common genes, alongside the number of genes tested and a scatterplot. Spearman correlation shows whether genes tend to retain a similar rank; by itself, it does not show whether expression values agree in scale or whether a subset of genes behaves very differently.
Inspect gene-level residuals or fold differences as well. Look for systematic over- or under-estimation and for outlying genes that may be obscured by an overall coefficient. A single score can make broad agreement look more complete than it is.
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Published coefficients are context-bound, not pass/fail targets. In a breast cancer comparison using tTMA1 (2024), the 2025 imaging-platform benchmark reported Spearman coefficients of 0.64 for Xenium, 0.55 for MERSCOPE, and 0.80 for CosMx. The authors also reported variation across datasets and repeated over- or under-estimation for several genes. These values describe that particular tissue and comparison, not expected performance for other experiments. The same study is available at Nature Communications.
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Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →A separate 2023 benchmark found broadly similar correlations between the tested imaging-based spatial platforms and orthogonal RNA-seq datasets across its tested panels. It also cautioned that detecting more genes does not by itself establish whether the additional signal is biological or false positive. See the 2023 Nature Communications benchmark.
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Check replicate quality before interpreting disagreement
Assess each modality’s replicates and quality controls on their own before attributing a discrepancy to biology. ENCODE’s listed bulk RNA-seq standards specify two or more replicates and gene-level Spearman concordance above 0.9 for isogenic replicates and above 0.8 for anisogenic replicates in the contexts covered by those standards. These are ENCODE bulk RNA-seq standards, not universal acceptance thresholds for spatial transcriptomics or every experiment. Consult the ENCODE bulk RNA-seq standards for their scope.
How to interpret a mismatch
Disagreement does not automatically mean that one modality is wrong. Investigate possible explanations in the comparison itself and in each assay’s quality before drawing a biological conclusion:
- Sample and tissue composition: Check whether the specimens, tissue regions, and biological contexts are comparable. A region-level spatial profile and whole-tissue bulk profile may represent different mixtures of cells.
- Reference match: Confirm whether the bulk data are from matched specimens or a separate cohort, and account for differences in tissue context.
- Shared genes and identifiers: Verify gene mapping and which genes were retained in the comparison; a different overlap can change the result.
- Quantification and normalization: Review the values and processing used for both modalities before interpreting differences in scale.
- Assay sensitivity and segmentation: Consider whether detection limits or spatial cell-segmentation choices could affect the spatial profile.
- Replicate quality: Look for within-modality inconsistency before treating cross-modality differences as biological.
When the conclusion depends on localization, cell assignment, or absolute abundance, use a separate spatial or orthogonal validation appropriate to that claim. Cross-gene correlation is evidence about aggregate pattern agreement, not a complete assessment of spatial data quality.
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