OMICSDESKbioinformatics, delivered

Sample deliverable

A complete single-cell analysis, run end to end on a public dataset (10x Genomics PBMC 3k) so you can judge the work before you send us anything. Every number below was computed by the pipeline — nothing here is illustrative.

2,700cells in raw matrix
2,638cells after QC (−2.3%)
817median genes / cell
2.03%median mitochondrial
6clusters (Leiden 0.5)
0%unassigned cells

1 · Quality control

Per-cell distributions before filtering. Cells with <200 genes, >2,500 genes or >5% mitochondrial reads were removed.

QC violin plots

2 · Clustering

UMAP clusters

3 · Cell-type annotation

Clusters annotated from canonical marker panels, then checked against the ranked marker genes of each cluster.

UMAP annotated cell types
Cell typeCellsShare
CD4+ T119345.2%
CD14+ Monocyte63524.1%
NK42216.0%
B34012.9%
Dendritic351.3%
Platelet130.5%

4 · Marker genes

Marker gene dot plot

5 · Independent check: automated annotation, run blind

Marker-based labels are only as good as the person choosing the markers, so we run a reference-based classifier that never sees them — here CellTypist Immune_All_Low (public) — and compare. On this dataset the two methods agree on 6 of 6 clusters at the lineage level, and within every cluster the classifier assigned the same label to 100% of cells. Where the two disagree, that cluster gets a broader label and a sentence in the results rather than a confident name.

ClusterCellsMarker-basedAutomated (blind)Within-cluster consistency
01193CD4+ TTcm/Naive helper T cells100%
1422NKCD16+ NK cells100%
2340BB cells100%
3635CD14+ MonocyteClassical monocytes100%
413PlateletMegakaryocytes/platelets100%
535DendriticDC100%
Manual annotation Automated annotation

This is the part most analyses skip. It costs an hour and it is the single best defence against the reviewer question "how do you know these are cell type X?" — the method is written up here.

What you would receive

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