Bulk & Single-Cell RNA-Seq Analysis

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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.

Schematic: raw reads become a quantified matrix, then tested differences, then annotated populations — with the statistics matched to the study design.

Key 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

Identifying differentially expressed genes between treatment and control conditions

Characterising cellular composition and state changes in a tissue or tumour microenvironment

Discovering mechanism of action or resistance pathways for a compound

Finding biomarkers of response in a clinical or preclinical cohort

Mapping differentiation or activation trajectories in a developmental or immune system

Methods and Techniques Applied

Read quality control, adapter and contamination assessment, alignment or pseudo-alignment and quantification

Normalisation, batch correction and exploratory analysis including principal component and correlation structure

Differential expression using established count-based statistical models, with covariates and contrasts specified in advance

Functional interpretation by GO, KEGG, Reactome and GSEA with appropriate background sets

Single-cell processing: filtering, doublet detection, integration, clustering, marker identification and annotation

Advanced single-cell analysis: trajectory inference, RNA velocity, cell–cell communication and compositional testing

What We Need From You

Raw FASTQ files or a count matrix, with the sequencing platform and protocol described

A complete sample metadata table including group, batch, and any covariates such as sex or age

The biological question and the specific comparisons that matter

Species, reference genome preference and any custom annotations required

What You Receive

Quality control report covering every sample, with outliers identified and discussed

Normalised expression matrices and full differential expression tables with statistics

Pathway and gene set enrichment results with the analysis background documented

Single-cell objects in standard single-cell object formats delivered ready for your own further exploration

Publication-quality figures, analysis code and a methods section with software versions

What We Compute and Report

Per-sample quality metrics and normalised expression matrices

Differential expression tables with fold change, dispersion and adjusted significance

Pathway and gene set enrichment statistics with the background set documented

Cell type composition per sample with annotation evidence and ambiguity flagged

Trajectory, velocity and cell–cell communication metrics where the design supports them

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.

Specifications

Item	Specification	Notes
Bulk samples	4–500+	Minimum three replicates per group for meaningful statistics
Single-cell scale	Up to millions of cells	Across samples and integrated datasets
Typical turnaround	2–5 weeks	Single-cell projects at the longer end
Output	Tables, objects, figures, code	Including reproducible analysis scripts

Service Workflow

Step 01

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.

Step 02

Feasibility Review

We check that the data, target class and available structural evidence can support the proposed approach, and say plainly where they cannot.

Step 03

Technical Proposal

Scope, methods, deliverables, timeline and price are confirmed in writing, together with the limitations we expect to report.

Step 04

Computation and QC

The analysis runs on pinned tool versions in a containerised environment, with quality checkpoints and method comparison where that is informative.

Step 05

Report and Follow-Up

You receive the ranked results, the documented methods, the limitations and a proposed validation plan, plus support in interpreting them.

Is This the Right Service for You?

Choose this service 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

The failure modes that produce confident-looking results which do not survive the next experiment. Each one is designed against rather than discovered afterwards.

Pseudobulk aggregation within sample and cell type is used, because cell-level testing inflates significance dramatically.

Case Studies

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.

Questions We Are Asked Before Scoping

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 in Touch
about Bulk & Single-Cell RNA-Seq Analysis

Tell us about the target, the data you hold and the decision the result needs to support. A scientist reviews every enquiry and replies within one business day.

Feasibility reviewed before any scope or price is fixed

Methods, parameters, confidence and limitations in every report

Confidentiality agreement available before data is shared