Mocking data for understanding interactions in DESeq2
Entering edit mode
blawney • 0
Last seen 2.1 years ago

I am working on a more complex design, but I think a basic 2-factor (each with two levels) captures the spirit of my question. Namely, I am interested in creating "reasonable" mock data to appropriately test my understanding of extracting the appropriate contrasts in DESeq2's results method.

In the script below, I create some mock data using DESeq2's makeExampleDESeqDataSet method. I then perturb the several genes by quite a bit, "spiking" in the effects I hope to find upon running it through a differential expression analysis.

My hypothetical setup takes 2 factors (genetic background and treatment) at two levels each:

  • background factor has levels: sgCtrl, sgFoo
  • treatment factor has levels: DMSO, T1

There are 3 replicates in each combination of levels for a total of 12 total samples.

I model using the main + interaction: ~ background + treatment + background:treatment

resultsNames(dds) produces the following:

[1] "Intercept"                   "background_sgFoo_vs_sgCtrl" 
[3] "treatment_T1_vs_DMSO"        "backgroundsgFoo.treatmentT1"

Now, running results(dds, name='background_sgFoo_vs_sgCtrl'), I recover the main effect on background that I introduced, as expected. That is, the genes I perturbed are all near the top of the sorted list.

However, I cannot seem to recover any interaction term using res <- results(dds, name="backgroundsgFoo.treatmentT1"). For reference, here are the raw counts for the gene where I introduced the interaction effect:

 sample1  sample2  sample3  sample4  sample5  sample6  sample7  sample8 
     116      171      157      130       24      125      433      567 
 sample9 sample10 sample11 sample12 
     497      844      838      864

Even just visually, you can see the main background effect on samples 7-12 and the additional effect on samles 10-12, which I thought should yield a very obvious interaction effect. Yet that particular gene gets raw p-value 0.017 and FDR of 0.81, despite me introducing a rather large perturbation. Is this just an artifact of my spiking procedure? Or am I missing something?

Question/clarification 2

Given the resultsNames above, I can identify the following coefficients:

  • B0 = intercept (representing sgCtrl + DMSO here)
  • B1 = main effect of background (e.g. comparing sgFoo+DMSO vs. sgCtrl+DMSO)
  • B2 = main effect of treatment (e.g. comparing sgCtrl+T1 vs. sgCtrl+DMSO)
  • B3 = interaction effect (of T1 with sgFoo)

Then, if I was interested in the effect of treatment T1 in sgFoo versus treatment T1 in sgCtrl, I would have: (B0+B1+B2+B3) - (B0 + B2) = B1+B3 and would use results(dds, contrast=c(0,1,0,1)), correct? I recognize this has the main background effect B1, so the interpretation is a little more nuanced than comparing genetic backgrounds both under DMSO condition.

Thanks for any help/advice/etc.!

Script (DESeq2_1.26.0 but can provide full sessionInfo if necessary):


mock_dds <- makeExampleDESeqDataSet(n=10000, m=12)

# First extract the raw counts
raw_counts = counts(mock_dds)
print('Original raw counts:')

# setup annotations
background = rep(c("sgCtrl","sgFoo"),each=6)
treatment = rep(rep(c("DMSO","T1"),each=3),2)
sample_annotations = cbind(background, treatment)
rownames(sample_annotations)<-paste0('sample', 1:12)

# By setup, there should be no true effects so far.  
# For gene1, gene3, and gene5, add an effect due to the genetic background.
# To do this get the row mean for each of those genes and simply add that onto
# the sgFoo samples (for both DMSO and T1 treated conditions) 
selected_genes = c('gene1', 'gene3', 'gene5')
subset_means = floor(rowMeans(raw_counts[selected_genes,]))

# create arrays for the samples we will eventually select:
sgFoo_samples = paste0('sample', 7:12)
sgFoo_plus_treated_samples = paste0('sample', 10:12)

# Add the perturbation to the sgFoo samples (sample7-sample12). Here, we add a 3-fold increase:
raw_counts[selected_genes, sgFoo_samples] = raw_counts[selected_genes, sgFoo_samples] + 3*subset_means

# Add an additional perturbation for only gene3 on the sgFoo+treated samples (sample10-sample12)
raw_counts['gene3', sgFoo_plus_treated_samples] = raw_counts['gene3', sgFoo_plus_treated_samples] + 3*subset_means['gene3']

print('Perturbed raw counts:')

# Run DESEq2:
dds <- DESeqDataSetFromMatrix(countData = raw_counts,
                 colData = sample_annotations,
                 design = ~background+treatment+background:treatment)

dds <- DESeq(dds)

# To get the background effect imposed on sgFoo--
res <- results(dds, name='background_sgFoo_vs_sgCtrl')
resOrdered <- res[order(res$pvalue),]


# Check the interaction effect to see if gene3 pops up:
res <- results(dds, name="backgroundsgFoo.treatmentT1")
resOrdered <- res[order(res$pvalue),]
deseq2 deseq • 268 views
Entering edit mode
Simon Anders ★ 3.7k
Last seen 22 months ago
Zentrum für Molekularbiologie, Universi…

I've check your code, and it seems correct to me. Running your code, I've found that the interaction does show up but with a raw p value of just 0.01, which does not survive multiple-testing adjustment.

I've then increased the strength of the interaction effect from 3*subset_means['gene3'] to 10*subset_means['gene3'], and then I can see it.

I guess what this teaches us is that interaction effects have to be much stronger than main effects to become detectable.

ALternatively, I have also reduced the within-group variance, by setting dispMeanRel = function(x) 1/x + .01in makeExampleDESeqDataSet, and then, the interaction effect also becomes visible, already with your 3-fold change.

I don't think hat this is a weakness of DESeq2, but just statistics: interactions are hard to detect and you need either very stron effects, or many samples, or very low residual variance to see them.

Entering edit mode

I appreciate the perspective-- I was quite surprised at how large the effect size had to be. I do get a lot of questions regarding treatment effects that are very similar to this query and this makes it seem like it is almost impossible to detect them in the setting of typical 3 vs. 3 cell-line experiments.


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