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.