What you need
A gene × sample count matrix (raw counts) and a sample sheet with condition and any covariates such as batch, sex or subject for paired designs.
Step by step in RNA Atlas
- Import counts and sample metadata. Or try the Bulk RNA experiment example first.
- Review the design. In the Bulk model workbench, choose the factor of interest, covariates and pairing; the tool checks for confounding and missing levels.
- Filter and fit. Set an explicit count filter and fit limma-voom (optionally with sample quality weights and robust empirical Bayes) in the browser.
- Read the results. Volcano and MA plots, diagnostics and a results table with effects, moderated t, p and BH-adjusted values per contrast.
- Pathways and export. Run enrichment with bundled Reactome sets and export plots, tables, the methods text and an R script that reproduces the fit.
Good to know
- The browser engine uses library-size scaling options rather than TMM, and does not implement DESeq2 dispersion models or shrinkage; the optional engine on your own computer provides those methods.
- Biological samples are the replicates; technical replicates should be combined first.
Open RNA Atlas How it is validated
Questions
Is the browser fit the same as limma in R?
The browser runs limma in R compiled to WebAssembly; model results are checked against independent R fits — see the validation page.
Can I import results from DESeq2 instead?
Yes. You can import a differential-results table and continue with plots and pathway analysis.
Found something wrong or unclear? Write to contact@solveseverything.com. See also: how results are validated.