DESEq2 comparison with two conditions and paired experiments

Question: DESEq2 comparison with two conditions and paired experiments

4.2 years ago by

severine.vincent • 0

European Union

severine.vincent • 0 wrote:

Hi,

I have a question about an analysis of RNA seq data via DESeq2.
The RNA seq design is the following:

Sample experiment siRNA hormonal treatment

1 M0 sineg control

2 M1 sineg control

3 M2 sineg control

4 M0 sineg hormone

5 M1 sineg hormone

6 M2 sineg hormone

7 M0 siX control

8 M1 siX control

9 M2 siX control

10 M0 siX hormone

11 M1 siX hormone

12 M2 siX hormone

So there are two conditions (siRNA and hormonal treatment) and within each condition there are a control sample (sineg or control) and a treated sample (siX or hormone). And we realized 3 experiments (M0,M1,M2).

I would like to identify genes that respond differently to hormonal treatment in condition siX compared to the condition sineg. The design I used is the following: ~ hormonal treatment + si + hormonal treatment :si. Is it OK?

Finally I would like to take into account the experiment. In fact my experiments are paired. For example, a treatment for the experiment M0 must be compared with the control of the same experiment M0. So my question is: how do I set up the design for that?

Thanks in advance.

Severine

deseq2

ADD COMMENT • link •

modified 4.2 years ago by Michael Love ♦ 20k • written 4.2 years ago by severine.vincent • 0

4.2 years ago by

Michael Love ♦ 20k

United States

Michael Love ♦ 20k wrote:

hi Severine,

The design to use is then:

~ experiment + hormonaltreatment + siRNA + hormonaltreatment:siRNA

This will control for the experiment effect, so taking into account the pairing of samples.

As the interaction of interest is between two factors both with only two levels, I'd recommend the following:

dds = DESeq(dds, modelMatrixType="standard")
results(dds)

(The standard model matrix vs expanded doesn't make a big difference, but makes the analysis simpler in this case, reducing the interaction to a single term.)

You might also consider renaming the variables to reduce your keystrokes:

siRNA with levels neg, X

hormone with levels control, treated

And don't forget to set the base level for all factors (see vignette).

ADD COMMENT • link modified 4.2 years ago • written 4.2 years ago by Michael Love ♦ 20k

Hi I have a similar setup of experiments (but with experiments not being paired)

I have two genotypes (geno): "A","B", Two treatments (treat): "control" "damage"

Four replicates for each genotype/treatment combination, 16 samples in total

I want to know whether the effect of "damage" compared to "control" is different between genotypes.

So I used the following design: ~geno+treat+geno:treat

and then, following the example in the ?results section of the DESeq2 vignette, I used

results <- results(dds,name="genoB.treatdamage")

However, no DEGs could be identified (all adj. p values had the same value: 0.9999928)

This is in contrast to another approach:

I first determined DEGs between genoA_control and genoB_control (comparison 1), next I determined DEGs between genoA_damage and genoB_damage (comparison 2).

Then I made a Venny, and there were 8 DEGs (adj. pvalue < E-5) in comparison 2 that were not present in comparison 1 and I also validated this by checking read coverage of these 8 DEGs in IGV.

So, I'm wondering what I should do...

Regards

Wannes

ADD REPLY • link modified 9 days ago • written 10 days ago by wd • 10

The first procedure is a statistical test of the interaction and is the recommended test. The second is an ad-hoc procedure without FDR control on the resulting set. Sometimes intersecting FDR sets is the only option, and the resulting intersection set does not have FDR control, but here there is a statistical test that gives you the correct answer with FDR control.

ADD REPLY • link written 9 days ago by Michael Love ♦ 20k

Dear Prof. Love

Thank you for your reply. I guess I'll have to perform more downstream experiments whether the DEGs of the ad-hoc procedure are meaningful.

Just to give you an idea: this is a gene that is not significantly downregulated (based on FDR) according to the recommend test (test of interaction), but based on the intersection approach this is one of the few DEG that pops up (adj. p value < e-30) in comparison 2 and not in comparison 1.

'genoB.treatdamage"

"ID" "name" "log2FoldChange" "baseMean" "lfcSE" "stat" "pvalue" "padj"
X" "protein" -1.88972972182758 1366.46086631548 0.880800838900554 -2.14546766802176 0.0319154738120772 0.999992751003146

"genoA_damage vs genoB_damage" ad-hoc procedure

"ID" "name" "log2FoldChange" "baseMean" "lfcSE" "stat" "pvalue" "padj"
X protein -0.99663284 2805.129516 0.087926543 -11.33483477 8.82E-30 9.79E-26

It is a gene that is lowly expressed in control of geno A and genoB (reads 10-20) while being highly expressed upon treatment of geno A (reads 800-900) and geno B (reads 1700-1800).

Regards

Wannes

ADD REPLY • link modified 9 days ago • written 9 days ago by wd • 10

You are highlighting that the interaction is not significant while the main effect in the "damage" group is significant. There is nothing inconsistent here.

ADD REPLY • link written 9 days ago by Michael Love ♦ 20k

Similar posts • Search »