OMICSDESKbioinformatics, delivered

Free: we analyse your single-cell data, not just QC it

Upload a count matrix and get back the QC every tool gives you — per-cell distributions, data-driven thresholds (median ± 3 MAD, not copied from a tutorial), a barcode-rank plot and the genes that dominate your counts — and then the analysis itself: clustering, a UMAP and automated cell-type annotation of your own data, typically within two minutes. No account, no email required. Your file is deleted the moment the analysis finishes.

Cannot share the file? Type five numbers instead — assessed in your browser, nothing uploaded.
No data to hand? Open an example report — the same output, run on the public 10x PBMC 3k dataset.

bulk count matrix too — if the file turns out to be a bulk RNA-seq matrix (a few dozen samples rather than thousands of cells) we detect that and run the bulk checklist instead: library sizes, detected genes, sample correlation, PCA outliers and a gene-filtering rule.

Runs on our own machines, typically 30 seconds to 3 minutes depending on size. Nothing is stored: the input is removed as soon as the report is generated, and the report itself expires after 7 days.

What you get

Per-cell distributions

Genes, UMIs and mitochondrial fraction per cell, with the suggested cut-offs drawn on top.

Thresholds that fit your data

Computed from your own distributions rather than the standard 200 / 2500 / 5% that fails on many tissues.

Warnings worth acting on

Missing mitochondrial annotation, undetected empty droplets, suspiciously shallow profiles, over-aggressive filtering.

Clustering and annotation

Not just QC: your data clustered, embedded and labelled by cell type — the same pipeline that starts a paid project, run on a subsample so it finishes while you wait.

A PDF you can forward

Everything above as a document for your supervisor or core facility. No attribution required.

→ Why fixed QC thresholds fail, and what to use instead