Single-Cell Omics
Resolving biology one cell at a time — scRNA-seq and scATAC-seq to discover cell types, states and trajectories.
What this area is.
Bulk assays average over millions of cells and hide the biology that matters. Single-cell omics measures expression and chromatin per cell, revealing rare populations, transitional states and the structure of tissues.
We run the full pipeline — QC, normalisation, batch correction, clustering, UMAP embedding, annotation and trajectory inference — to turn cell-by-gene matrices into interpretable cell atlases.
Tools & technologies
What we do.
Core methods we apply in single-cell omics.
scRNA-seq
From count matrices to annotated cell types.
scATAC-seq
Single-cell open-chromatin and regulatory landscapes.
QC & integration
Doublet removal, normalisation and batch correction.
Clustering & embedding
Graph clustering with UMAP / t-SNE visualisation.
Cell-type annotation
Marker- and reference-based labelling.
Trajectory inference
Pseudotime and lineage reconstruction.
From data to insight.
How a single-cell omics project flows end to end.
Single-cell prep
droplet / plate
Count matrix
cell × gene
QC
doublets · filters
Integrate
batch correction
Cluster
Leiden + UMAP
Annotate
cell types · trajectories
Publication-grade figures.
Interactive, live-rendered visualisations used in single-cell omics.
Where we go deep.
Tumour heterogeneity
Subclones and microenvironment at single-cell resolution.
Immune microenvironment
Profiling immune populations and their states.
Developmental trajectories
Reconstructing how cell states unfold over time.
Questions we answer.
A few of the things people ask about single-cell omics — and our short answers. Ask CGB-AI for more.
Why single-cell over bulk?
It exposes rare cell types, mixtures and transitions that bulk averaging erases — essential for heterogeneity and microenvironment studies.
What does UMAP show?
A 2-D map where nearby points are similar cells; clusters suggest cell types or states, validated with markers.
Publications in Single-Cell Omics.
Drawn from our full record of 173 papers, filtered to this area.
Start a single-cell omics project.
Tell us the biological question and the data you have — we will map out an approach.