OMICSDESKbioinformatics, delivered

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.

12samples in series
27,179genes in matrix
16,110genes tested after filtering
2,414up (padj<0.05, |LFC|>1)
2,340down
2 vs 2luminal lactating vs luminal virgin

1 · Design, taken from the source metadata

SampleCell typePhysiological state
MCL1-DGbasalvirgin
MCL1-DHbasalvirgin
MCL1-DIbasalpregnant
MCL1-DJbasalpregnant
MCL1-DKbasallactate
MCL1-DLbasallactate
MCL1-LAluminalvirgin
MCL1-LBluminalvirgin
MCL1-LCluminalpregnant
MCL1-LDluminalpregnant
MCL1-LEluminallactate
MCL1-LFluminallactate

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.

PCA of samples

3 · Differential expression (DESeq2)

Volcano plot
Genelog2 fold changeadjusted p
Gsdmc11.842.92e-15
Csn1s2b11.460
Plpp411.430.00000653
Glycam111.20
Atp2b210.683.59e-213
1700042G15Rik10.470.0000421
Wap10.410
Hephl110.141.23e-169
Fabp310.110
Umodl110.080.0000887
Gm83489.890.000127
Gm164409.741.71e-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

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