Spatial Transcriptomics Data Analysis Services That Prevent Critical Failures

Many spatial transcriptomics projects collapse not because of lack of effort, but due to subtle missteps that compound across preprocessing, alignment, and interpretation stages. Platforms like 10X Visium, Xenium, and CosMx offer immense power - but also invite failure if handled without care. Our team brings unmatched expertise in spatial transcriptomics bioinformatics, combining deep biological insight with computational precision.

At AccuraScience, we support researchers in navigating spatial transcriptomics data analysis - from raw images to robust biological insight. We’ve rescued projects undermined by faulty alignment, misused deconvolution tools, and overclaims on spatially variable genes. And we’ve helped teams rebuild pipelines that can survive peer review and lead to publication.

The Most Common Ways Spatial Transcriptomics Projects Fail

We’ve analyzed and rescued dozens of spatial transcriptomics datasets. Many errors are subtle - but devastating. Here are a few of the most common problems we help solve:

1. Misaligned Images and Misplaced Spots

Default image registration in pipelines like Space Ranger is often accepted without visual inspection. A few microns of offset can flip your interpretation - immune infiltration at tumor edge may actually map to stroma. We manually inspect and correct every alignment.

2. QC Filters That Remove Real Signals

Generic thresholds like high mitochondrial reads or low UMI count erase edge biology - where immune response or hypoxia reside. We adapt thresholds by histological zone, not textbook cutoffs.

3. False Confidence from Marker Maps

Visualizing a single marker gene in isolation is misleading. We support every spatial claim with statistics, validate gradients, and avoid drawing stories from noise.

4. Deconvolution Errors from Mismatched References

Using scRNA-seq data from the wrong species, age, or protocol can place neurons in muscle or T-cells in fibroblast zones. We vet references carefully and validate inferred cell types spatially.

5. Overclaims on Spatially Variable Genes

SVG detection tools will always return results - even from noise. We combine multiple metrics, validate against histology, and test pathway enrichments before drawing biological conclusions.

6. Tissue Warps and Tears That Break Spatial Continuity

Spatial platforms assume evenly sliced, intact sections - but distortions during embedding or staining introduce subtle warps. These lead clustering tools like SpaGCN to draw misleading spatial boundaries. We detect and mask tissue folds, and correct geometry when necessary.

7. Clusters That Look Spatial – But Aren’t

Spatial clustering tools enforce locality even when expression is noisy. We've seen beautiful but false clusters that collapse under biological scrutiny. We validate all spatial domains against known tissue zones and expression signatures.

8. Reviewer Requests That Break Fragile Workflows

Many spatial analyses are done through interactive tools or undocumented scripts - making revision impossible when reviewers ask for reanalysis. Our pipelines are modular, version-controlled, and fully reproducible - even months later.

Read Our Experts' Blogs

Our in-depth, 2-part blog series highlights 10 critical pain points - from misalignments and noisy spots to poor deconvolution and reviewer-driven re-analysis.

• Part 1: From Slide to Signal - Learn how small preprocessing errors cascade into false discoveries
• Part 2: From Interpretation to Collapse - Discover how to prevent overclaims, reviewer pushback, and reproducibility traps

Why Researchers Trust AccuraScience

Founded in 2013, AccuraScience was the first bioinformatics service company in the U.S. offering broad-spectrum customized solutions to academic and industry researchers.

Our team of senior bioinformaticians brings over 200 years of combined experience - with deep biological insight and computational rigor. We’ve completed projects for over 180 research institutions across five continents, contributed to NIH-funded grants, and supported peer-reviewed publications and clinical applications.

Our senior bioinformaticians bring deep domain knowledge in spatial transcriptomics bioinformatics and strong track records in image processing, histology-aware QC, and reproducible pipelines. We support Visium, CosMx, and Xenium - and have helped teams across oncology, neuroscience, developmental biology, and more.

Whether you need full-service spatial transcriptomics data analysis or help recovering from reviewer feedback, we can help.

Explore our full blog series on spatial transcriptomics bioinformatics challenges to understand how we work - and how we help projects succeed under scrutiny.