Ref H5
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Spatial transcriptomics keeps the tissue architecture that dissociation-based methods discard, which is exactly what matters when the question concerns tumour boundaries, immune infiltration or niche organisation. We process sequencing-based and imaging-based spatial data, map expression onto histology, deconvolve or annotate cell identity per location, identify spatial domains, and analyse the cell–cell communication that the spatial arrangement makes plausible.
Key Features & Advantages
Histology Kept In The Loop
Expression results are registered to and interpreted alongside the tissue image, because spatial conclusions that ignore the histology are frequently wrong.
Resolution Handled Honestly
Spot-level platforms mix several cells per measurement; we deconvolve with reference data and report the uncertainty instead of presenting spots as if they were cells.
Domains Defined Statistically
Spatial domains are identified using methods that use neighbourhood structure explicitly, rather than by clustering expression and hoping the result is spatially coherent.
Neighbourhood-Aware Communication Analysis
Ligand–receptor analysis is restricted to cell pairs that are actually adjacent, which is the main advantage spatial data holds over dissociated single-cell data.
Where This Service Is Used
1Characterising the tumour microenvironment and the invasive margin
2Mapping immune cell infiltration relative to tumour and stromal compartments
3Identifying niche structures in developmental or regenerating tissue
4Relating drug response or resistance to spatial position within a tissue
5Validating single-cell findings in their original spatial context
Methods and Techniques Applied
1Platform-specific preprocessing and quality control, with image registration to the expression grid
2Normalisation accounting for spot or cell capture efficiency and tissue-dependent variation
3Cell type deconvolution for spot-based platforms or segmentation and annotation for imaging platforms
4Spatial domain identification using neighbourhood-aware clustering methods
5Spatially variable gene detection and spatial statistics across regions of interest
6Neighbourhood-constrained cell–cell communication and co-localisation analysis
What We Need From You
1Raw spatial data with the accompanying histology images and platform details
2Matched single-cell reference data where available, which substantially improves deconvolution
3Pathologist annotations or regions of interest, if any have been defined
4The biological question, particularly which spatial relationships are of interest
What You Receive
1Processed spatial objects ready for your own further exploration
2Cell type composition maps overlaid on the tissue image
3Spatial domain identification with characteristic gene sets per domain
4Spatially variable gene lists and region-to-region comparison statistics
5Cell–cell communication analysis restricted to spatially plausible interactions, with figures
What We Compute and Report
1Per-spot or per-cell expression matrices registered to the tissue image
2Cell type composition estimates per location with uncertainty reported
3Spatial domain assignments with the genes characterising each domain
4Spatially variable gene statistics and region-to-region comparisons
5Neighbourhood-constrained ligand–receptor interaction scores
Questions We Are Asked Before Scoping
Is spot-level data single-cell resolution?
▸Not for capture-based platforms, where each spot typically contains several cells. Deconvolution estimates composition and its uncertainty; it does not recover true single-cell measurements.
Do we need matched single-cell data?
▸It is not mandatory, but deconvolution accuracy improves considerably with a matched reference. Where none exists we use a public reference and report the added uncertainty.
How many sections are needed?
▸Spatial experiments are expensive, so replication is often minimal, which limits statistical claims. We state what the available replication supports and design analyses accordingly.
Interpretation and Limitations
Computational predictions prioritise hypotheses; they do not independently establish binding, biological activity, therapeutic efficacy, safety or clinical validity. Performance depends on input quality, target class, the structural evidence available and the applicability domain of the selected methods. Scores used for ranking are comparative devices and should not be read as measured quantities. All deliverables are for research use only and are not intended for diagnostic or therapeutic use.
Is This the Right Service for You?
Choose this when
Tissue architecture is part of the biological question
Immune infiltration relative to tumour compartments must be quantified
Single-cell findings need placing back into their spatial context
Consider another service when
Cell-type resolution matters more than position
Bulk & Single-Cell RNA-Seq Analysis Protein localisation is the actual readout required
Medical Imaging, Radiomics & Pathomics What Goes Wrong, and How We Handle It
These are the failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.
Spot-level data presented as single-cell resolution
Capture-based platforms mix several cells per spot; deconvolution estimates composition and its uncertainty rather than pretending otherwise.
Spatial claims made from one or two sections
What the available replication supports is stated before analysis, and conclusions are scoped to it.
Communication analysis between cells that are nowhere near each other
Ligand–receptor analysis is restricted to spatially adjacent pairs, which is the advantage the data actually provides.
A Worked Example
Mapping the invasive margin of a tumour
The situation
The team needed to know whether an immune population sat inside the tumour or was excluded at the boundary.
What the work involved
Expression was registered to histology, cell types were deconvolved with a matched single-cell reference, spatial domains were identified with neighbourhood-aware methods, and distance-to-margin statistics were computed.
What came back
The population was concentrated in a narrow band outside the tumour boundary, consistent with exclusion rather than infiltration — changing the interpretation of the accompanying bulk data.
Illustrative of the shape this work takes. Details are generalised, no client or target is identified, and scope and outcome vary with the system in question.
Service Workflow
1
Scientific Consultation
We discuss the objective, the decision it supports and the data you hold. Nothing is quoted before we understand what the result has to answer.
2
Feasibility Review
We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.
3
Technical Proposal
Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.
4
Computation and QC
The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.
5
Report and Follow-Up
You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.
Service Process
1
Online Consultation
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2
Solution Matching
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3
Service Contract
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4
Project Execution
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5
Data Delivery
Specifications & Turnaround
Note: ICDMO delivers computational (in silico) services. Results prioritise hypotheses for testing; they do not by themselves establish binding, biological activity, efficacy or safety. All deliverables are for research use only and are not intended for diagnostic or therapeutic use. Downstream experimental work is scoped separately and executed through qualified partners.
Get a Custom Quote
Our scientific team responds within 24 hours with a detailed technical proposal and pricing tailored to your project.
Contact Us Online ConsultationStandard Deliverables
✓Sequence-verified final product
✓Full QC documentation
✓Certificate of Analysis (CoA)
✓Electronic data files
✓Project completion report
Why ICDMO?
Computational CDMO Focus
Reproducible, Versioned Pipelines
Confidence & Limitations Reported
Experiment-Aware Recommendations
Response Within One Business Day