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Question: DESEq2 comparison with two conditions and paired experiments
0
4.2 years ago by
European Union
severine.vincent0 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?

Severine

modified 4.2 years ago by Michael Love20k • written 4.2 years ago by severine.vincent0
1
4.2 years ago by
Michael Love20k
United States
Michael Love20k 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).

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

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.

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

"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