Single-cell QC report

Input: public 10x PBMC 3k dataset (example run) · generated by the free QC tool at omics.hstgenomics.com · the input file was deleted once this report was built.

2,700cells / barcodes

32,738genes

817median genes / cell

2,197median UMIs / cell

2.03%median mitochondrial

6.6%removed by suggestion

Suggested thresholds for this dataset

Filter	Suggested	Common tutorial default
Minimum genes per cell	232	200
Maximum genes per cell	1,402	2,500
Maximum mitochondrial %	5%	5%
Cells retained	2,523 of 2,700

Suggestions are median ± 3 MAD of this dataset's own distributions, floored at 200 genes. They are a starting point; tissue biology beats any formula.

Things worth a second look

No structural problems detected in these metrics.

Expected doublets from loading: about 58 barcodes (2.1%). A gene-count ceiling will not remove them — a doublet detector will.

Distributions

UMIs against mitochondrial fraction — dying cells sit low-count / high-mito.

Barcode rank plot: a sharp knee means cells and empty droplets separate cleanly.

Most abundant genes

The top 10 genes carry 14.2% of all counts.

Genes carrying the largest share of counts.

Gene	% of all counts
MALAT1	2.53%
TMSB4X	1.94%
B2M	1.9%
RPL10	1.39%
RPL13	1.21%
RPL13A	1.2%
FTL	1.17%
RPS2	1.02%
RPS6	0.98%
FTH1	0.9%

What this does not cover

Doublet detection, ambient-RNA correction, clustering and annotation are not part of this free report.
Medians hide distributions; a real assessment looks per sample and at the joint distributions.

If you want that done rather than described

Standard single-cell analysis of one sample like this is $380–$650; advanced modules (integration, trajectory, cell–cell communication, differential abundance) are quoted per project. You get a fixed written quote before any work starts, and the scripts that produced every figure come with the delivery.
→ omics.hstgenomics.com/quote · no-upload QC self-check: omics.hstgenomics.com/qc-self-check