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.
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?
Thank you for your prompt reply and help.
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?