Sample deliverable — bulk RNA-seq
A differential expression analysis run end to end on public data (GEO GSE60450, mouse mammary gland). Sample groups were read from the GEO metadata rather than assumed, and the result carries its own biological check.
Do this on your own data: the design here is balanced and complete; whether yours supports the contrast you actually want is settled before any software runs. Price a bulk differential-expression project by sample count in about a minute: omics.hstgenomics.com/cost-calculator. What bulk data can and cannot tell you about cell-type composition: omics.hstgenomics.com/sample-deconvolution.
Want it done rather than shown? We do analysis only — send a counts table and the sample sheet and get the differential expression, the enrichment and the code that produced them, at a fixed price agreed before any work starts: omics.hstgenomics.com/quote.
1 · Design, taken from the source metadata
| Sample | Cell type | Physiological state |
|---|---|---|
| MCL1-DG | basal | virgin |
| MCL1-DH | basal | virgin |
| MCL1-DI | basal | pregnant |
| MCL1-DJ | basal | pregnant |
| MCL1-DK | basal | lactate |
| MCL1-DL | basal | lactate |
| MCL1-LA | luminal | virgin |
| MCL1-LB | luminal | virgin |
| MCL1-LC | luminal | pregnant |
| MCL1-LD | luminal | pregnant |
| MCL1-LE | luminal | lactate |
| MCL1-LF | luminal | lactate |
2 · Sample clustering
Before testing anything, check that samples group by biology. If replicates do not sit together, the differential expression that follows is not worth running.
3 · Differential expression (DESeq2)
| Gene | log2 fold change | adjusted p |
|---|---|---|
Gsdmc | 11.84 | 2.92e-15 |
Csn1s2b | 11.46 | 0 |
Plpp4 | 11.43 | 0.00000653 |
Glycam1 | 11.2 | 0 |
Atp2b2 | 10.68 | 3.59e-213 |
1700042G15Rik | 10.47 | 0.0000421 |
Wap | 10.41 | 0 |
Hephl1 | 10.14 | 1.23e-169 |
Fabp3 | 10.11 | 0 |
Umodl1 | 10.08 | 0.0000887 |
Gm8348 | 9.89 | 0.000127 |
Gm16440 | 9.74 | 1.71e-10 |
4 · The check that matters
A gene list is only trustworthy if it recovers something already known. Comparing lactating against virgin luminal cells should put milk-protein genes at the top — and it does: Csn1s2b, Glycam1, Lao1, Wap appear among the strongest up-regulated genes. We build a check like this into every project, and we tell you when it fails.
What a full project adds
- GO / KEGG / GSEA enrichment on the result, with the background set stated.
- Covariate handling — batch, sex, donor — in the model rather than after it.
- Results tables per contrast, and figures at submission quality.
- The script, parameters and package versions, so the analysis can be re-run.