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Question: Need help setting up DESeq2 complex model for multi-dose experiment
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16 months ago by
stwestreich0 wrote:

Hello,

I have RNAseq data from a longitudinal study, where six participants provided samples first at 0 dose, then at a low dose (25) of medication, and then at a high dose (35) of the same medication.  There are no massive changes that immediately jump out from the change in dosage, so I want to create a more complex DESeq2 model that will also look for significant changes occurring only in some of the individuals.

I've looked at the RNA time course vignette in the manual (https://www.bioconductor.org/help/workflows/rnaseqGene/#time-course-experiments), and while I want to look at that interaction term (interactions between dose and individual), I have more than 2 strains.  How can I take this into account?

If I only wanted to know about changes due to dosage level across ALL individuals, I'd run something simpler, like this:

ddsSimple <- DESeqDataSetFromMatrix(data_table_filtered, coldata, ~ Dose)

But I want to also capture (with a low p-value) any changes seen in one individual but not others.  Therefore, my current approach looks like:

ddsBMO <- DESeqDataSetFromMatrix(data_table_filtered, coldata,            design = ~ Individual + Individual:Dose + Dose) dds <- DESeq(ddsBMO, test="LRT", reduced = ~ Individual )

1. Will this approach provide low p-values for individual-specific changes due to dose, namely where one individual shows a response to the increased dose even when other individuals reflect no change?

2.  If this approach isn't correct, how can I evaluate for individual-specific changes while still including all samples?

Many thanks for any help and guidance.

modified 16 months ago by Michael Love18k • written 16 months ago by stwestreich0
1
16 months ago by
Michael Love18k
United States
Michael Love18k wrote:

I understood at first, 6 participants, each with three measurements. But then you mentioned strain, which I don't follow how that fits in. And you mentioned wanting to look at dose x individual interactions, which don't seem possible given that you can't produce biological replication from a single individual at a given dose. Can you show what the colData looks like so it is easier to understand the experimental design?

Hi Michael,

Ah, my mistake - in the "fission" example in the linked vignette, the interaction term is looking for strain-specific interactions.  There's no "strain" term in my own data.

My colData looks like this:

Dose = rep(c(0, 0, 25, 35), times = 6) Individual = rep(c("ind4003", "ind4005", "ind4006", "ind4008", "ind4009", "ind4016"), each = 4) nameD <- colnames(data_table_filtered) coldata <- data.frame(row.names = nameD, Dose = factor(Dose), Individual = Individual)

For biological replicates, I have, from each individual, 2 samples at 0 dosage (controls), 1 sample at dose level 25, and one dose level at 35.  Unfortunately, this is the only provided data, so I can't add more.

Given that I don't have multiple samples from each individual at each dose, does this mean that I can only measure changes across all individuals, not on a per-individual basis?

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Yes, I don't think you have enough replication to make individual-specific dosage inference, because you have no samples which capture the within-individual variation in expression after you start giving the medication.

Hi Michael,

That makes sense.  What if I treated the dose as binary, using a simple "0" or "1" for whether the individual received a dose?  In that case, I should be able to evaluate for individual-specific changes due to dose.

In that instance, would I use:

dds <- DESeq(ddsBMO, test="LRT", reduced = ~ Individual + Dose )

to look for individual-specific changes from consuming the dose?