TranscriptomicsBulk RNA-seq & microarray
From raw reads or public expression matrices to biologically meaningful results, using your data or datasets from GEO and TCGA.
- Quality control, alignment and quantification, or normalisation for microarray data
- Differential expression with clear statistics, volcano plots, heatmaps, PCA
- Batch-effect assessment and correction
- Functional enrichment (GO, KEGG, Reactome, GSEA)
- Cross-dataset validation to strengthen your findings
Single-cell transcriptomicsSee biology one cell at a time
- Quality control, doublet removal, integration across samples
- Clustering, UMAP and cell-type annotation
- Marker identification and differential expression between cell states
- Trajectory and pseudotime analysis
- Cell-cell communication (ligand-receptor) analysis
- Re-analysis of published scRNA-seq datasets to answer your question
Gene-specific single-cell analysisYour gene's cellular story
You have a gene or a panel. We tell you its cellular story. Ideal when a bulk analysis points to candidate genes and you need to know where they act.
- Which cell types express your gene, and in what proportion
- Expression shifts between disease and control, or treated and untreated
- Co-expression partners and cell-state associations
- Candidate biomarker localisation within the tumour or tissue microenvironment
- Figures designed to support the "single-cell validation" section of your paper
EpigenomicsRegulation behind the expression
- DNA methylation analysis (array and sequencing-based), including TCGA/GEO methylation data
- Linking methylation state to gene expression and outcome
Multi-omics integrationOne disease, many layers, one coherent story
Combine transcriptomic, methylation, proteomic, metabolomic and clinical data to find signals no single layer can show.
- Integration strategy matched to your data and question
- Shared biomarkers across layers and across diseases
- Network-level interpretation of integrated findings
Immune analysisDecode the immune landscape inside your samples
- Immune cell deconvolution from bulk expression (CIBERSORT, ssGSEA and related methods)
- Immune infiltration profiles across groups, subtypes or risk scores
- Association of genes or signatures with immune cell abundance
- Links between immune state, biomarkers and clinical outcome
Machine learning & ROC-AUC validationFrom a gene list to a defensible biomarker panel
- Feature selection (LASSO, random forest, SVM-RFE and others)
- Diagnostic and prognostic model building with proper train/test or cross-validation
- ROC-AUC analysis with confidence intervals, sensitivity and specificity
- Validation across independent datasets
Pathway analysis & PPI networksUnderstand the biology, not just the list
- GO, KEGG, Reactome and GSEA enrichment
- PPI network construction, hub gene identification (STRING, Cytoscape)
- Co-expression networks (WGCNA)
- Regulatory networks (transcription factors, miRNA, lncRNA where relevant)
- Clear network figures ready for journals
Public data mining (GEO & TCGA)No samples? No problem.
Our most popular route for researchers without in-house sequencing data. We design a study around public datasets: identify suitable cohorts, harmonise them, analyse, validate in independent datasets, and deliver a complete analysis package ready to be written up.
ConsultingGet the design right before you spend
- Study design review: sample size, controls, batch design, and which assay answers your question
- Pipeline and analysis audit: an independent second opinion on results you already have
- Target and biomarker prioritisation: in-silico triage of candidates from public data before wet-lab validation
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