Surrogate variable analysis for paired RNA seq experiment
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nhaus ▴ 40
@789c70a6
Last seen 1 day ago
Switzerland

Hi all,

I have an RNAseq experiment with 1000s of samples. Each sample comes from a patient and was treated either with a drug or DMSO (as control). I am currently deciding between SVA and RUVseq to account for "hidden" technical variation. My question is, if I have identified surrogate variables (regardless of method) and include them in my model like this:

design = ~ X1 + X2 + X3 + X4 + X5 + X6 + X7 + ... + condition

Will the surrogate variables also account for the fact that I have a "paired" design (each sample has a control and a treated counterpart)?

Any insights are very much appreciated!

Cheers!

RUVSeq sva • 320 views
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Following up somewhat on what James already said in Not fully paired differential gene expression analysis -- if you do a paired analysis then the comparison is within donor and you would not need to correct for "hidden" factors such age and sex -- the pairing takes care of that. Do you have a good data-driven reason to believe that additional correction is necessary? With thousands of samples you might simply removing the incomplete pairs and go with paired analysis + weighting as I suggested in linked post.

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Thank you very much for your comment! The reason I am thinking of going with the "SVA approach" is that I suspect that there are technical differences between the samples that are not recorded in the mdata (for example the differentiation time or purity of the cell culture). I believe that this might be the case because PC1 capture quite a lot of variation (30%) but I am unable to assign it to any column in the metadata and the experimental condition that I am interested in is not associated with PC1 and PC2. After running SVA, I get a nice separation for my experimental groups. I know that this is expected given that I explicitly tell SVA what biological condition I am interested in.

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for example the differentiation time or purity of the cell culture

That also would be captured by the pairing, no? Are the samples per donor processed in the same batch/run/time or is there a delay?

because PC1 capture quite a lot of variation (30%)

Just to be sure, that is PCA after regressing the donor effect, is it?

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Are the samples per donor processed in the same batch/run/time or is there a delay?

All samples from the same donor are processed and differentiated and treated together, but for each batch, there are several donors (for example Donor A, B and C are treated together in batch1). This means that by adjusting for donor, I am also adjusting for batch right? I think it might still be the case, that one cell line took longer to be differentiated into the cell subtype of interest. I will ask my collaborator.

Just to be sure, that is PCA after regressing the donor effect, is it?

After regressing out donor effect using limma PC1 captures 22% of variance and it is very difficult to determine what PC1 and PC2 actually corresponds to. PC3 and PC4 very nicely separate my biological groups. If I use UMAP project, I also get a nice seperation on UMAP1 and UMAP2 according to my biological groups.

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Sounds to me that including donor should capture a lot of relevant confounders and you should be good to go. What is the problem actually, don't you get DEGs when including donor without further corrections?

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I do get differentially expressed genes, but I honestly have never had a situation where I could not explain what PC1 and PC2 corresponds to. With the SVA approach I was able to do that, however, doing it like that the explained variance of PC1 was reduced to just 7% which seems very low...

If I decide to just include donor in my model as a covariate, does this mean that I have to make sure that each donor has an observation for each biological condition of interest (i.e. no incomplete observations)? I am asking, because if I run DESeq2 with incomplete observation, it still works and the linear model fitting works without any problem. Here is some example code for that:

library(DESeq2)

mdata <- data.frame(
    patient = rep(c("A", "B", "C", "D"), 2),
    condition = rep(c("treat", "ctrl"), each = 4)
)

# remove ctrl from D to simulate "dropout", i.e. unpaired sample
mdata <- mdata[-8, ]

# random counts
counts <- matrix(round(runif(700) * 100), ncol=7)

dds <- DESeqDataSetFromMatrix(
    countData = counts,
    colData = mdata,
    design = ~ 0 +patient + condition
)

res <- DESeq(dds)
results(res)
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