Ref H1
Computational Service
Confidence Reported
Scoped After Feasibility Review
Service Overview
Transcriptomic analysis is straightforward to run and easy to run badly. We handle both bulk and single-cell RNA-seq from raw reads through to biological interpretation: quality control, alignment and quantification, differential expression with an appropriate statistical model, and pathway-level interpretation. For single-cell data we add integration, clustering, annotation and trajectory analysis. Experimental design and batch structure are examined first, because no amount of downstream sophistication rescues a confounded comparison.
Key Features & Advantages
Design Reviewed Before Analysis
Replication, batch structure and confounding are assessed at the start. Where design limits what can be concluded, we say so before the analysis rather than in the discussion section.
Statistics Matched To The Data
Model choice, covariate handling and multiple-testing correction are selected for your design instead of applied from a default pipeline, and the choices are documented.
Cell Annotation With Evidence
Single-cell clusters are annotated using reference mapping and marker evidence together, with ambiguous or mixed populations labelled as such rather than given a confident name.
Publication-Ready Output
Figures are delivered as editable vector files with the methods text and software versions needed for a manuscript or regulatory submission.
Where This Service Is Used
1Identifying differentially expressed genes between treatment and control conditions
2Characterising cellular composition and state changes in a tissue or tumour microenvironment
3Discovering mechanism of action or resistance pathways for a compound
4Finding biomarkers of response in a clinical or preclinical cohort
5Mapping differentiation or activation trajectories in a developmental or immune system
Methods and Techniques Applied
1Read quality control, adapter and contamination assessment, alignment or pseudo-alignment and quantification
2Normalisation, batch correction and exploratory analysis including principal component and correlation structure
3Differential expression using established count-based statistical models, with covariates and contrasts specified in advance
4Functional interpretation by GO, KEGG, Reactome and GSEA with appropriate background sets
5Single-cell processing: filtering, doublet detection, integration, clustering, marker identification and annotation
6Advanced single-cell analysis: trajectory inference, RNA velocity, cell–cell communication and compositional testing
What We Need From You
1Raw FASTQ files or a count matrix, with the sequencing platform and protocol described
2A complete sample metadata table including group, batch, and any covariates such as sex or age
3The biological question and the specific comparisons that matter
4Species, reference genome preference and any custom annotations required
What You Receive
1Quality control report covering every sample, with outliers identified and discussed
2Normalised expression matrices and full differential expression tables with statistics
3Pathway and gene set enrichment results with the analysis background documented
4Single-cell objects in standard single-cell object formats delivered ready for your own further exploration
5Publication-quality figures, analysis code and a methods section with software versions
What We Compute and Report
1Per-sample quality metrics and normalised expression matrices
2Differential expression tables with fold change, dispersion and adjusted significance
3Pathway and gene set enrichment statistics with the background set documented
4Cell type composition per sample with annotation evidence and ambiguity flagged
5Trajectory, velocity and cell–cell communication metrics where the design supports them
Questions We Are Asked Before Scoping
How many replicates do we need?
▸Three per group is the practical minimum for bulk differential expression and five or more is considerably safer. For single-cell work, biological replication at the sample level matters more than cell numbers.
Can you reanalyse data another provider generated?
▸Yes, and it is common. We start from raw data where possible so that quality control and processing choices are visible rather than inherited.
Will you find differentially expressed genes?
▸Only if they exist and the design has power to detect them. A null result is reported as a null result, with a power assessment explaining what effect size the study could have detected.
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
A sequencing provider delivered raw data and a minimal report
Cell-type composition may be confounding an apparent expression change
Results must survive peer review, with code and versions supplied
Consider another service when
Spatial arrangement within the tissue is central to the question
Spatial Transcriptomics Analysis Protein-level change is the real question
Proteomics & PTM Analysis 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.
Cells treated as independent replicates in single-cell statistics
Pseudobulk aggregation within sample and cell type is used, because cell-level testing inflates significance dramatically.
Batch structure confounded with the biological groups
Design and batch structure are reviewed before analysis, and we state what the data cannot support rather than proceeding regardless.
Clusters labelled with confident cell-type names on thin evidence
Annotation combines reference mapping with marker evidence, and ambiguous or mixed clusters are labelled as ambiguous.
A Worked Example
A treatment effect that turned out to be compositional
The situation
Bulk data showed strong upregulation of an immune signature after treatment, and the team planned a mechanism study around it.
What the work involved
Single-cell data from the same samples was processed, annotated with reference and marker evidence, and tested both compositionally and by pseudobulk within each cell type.
What came back
The signature reflected a threefold increase in infiltrating myeloid cells, not transcriptional induction. Within each cell type the pathway was unchanged, and the planned mechanism study was redirected.
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