Single-Cell Omics
Single-cell omics measures gene expression and chromatin accessibility in individual cells, bypassing the averages of bulk assays. We trace developmental lineages, map the immune microenvironment, and discover rare cell states using UMAP and clustering. This resolution reveals cellular heterogeneity in development and disease.
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 170 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.
