How to interpret different reduced models in time series analysis with DESeq2?

Question

[arshad1292]

Hello,

I am confused about interpreting the reduced models in complicated time series analysis. I am using the data described in the DESeq2 vignette as an example:

https://bioconductor.org/packages/release/workflows/vignettes/rnaseqGene/inst/doc/rnaseqGene.html#time-course-experiments

In this tutorial, they have the following factors:

1. minutes (4 levels: 0, 50, 100, 150)
2. strain (2 levels: wt, mut)

Their full model:

ddsTC <- DESeqDataSet(fission, ~ strain + minute + strain:minute)

And reduced model with LRT:

ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ strain + minute)

They described that the above model "performs a likelihood ratio test, where we remove the strain-specific differences over time. Genes with small p values from this test are those which at one or more time points after time 0 showed a strain-specific effect. Note therefore that this will not give small p values to genes that moved up or down over time in the same way in both strains".

So, this means genes with small p values will be those that are differentially expressed between strains over one or more time points? Did it regress out the individual effect of time and strain? and showing only the effect of interaction between strain and minute (strain:minute)?

Further, I have following few questions that will clarify my concept to a greater extent about this complicated time series analysis.

In the above example, I have one full model and 4 possible reduced models as shown below:

Full model:

ddsTC <- DESeqDataSet(fission, ~ strain + minute + strain:minute)

Reduced model 1:

ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ strain + minute)

Reduced model 2:

ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ minute)

Reduced model 3:

ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ strain)

Reduced model 4:

ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~ strain:minute)

Can someone please explain it to me in simple words how to interpret the above 4 reduced models?

I am confused about this complicated time series designs and believe if I understand how to interpret the reduced model correctly, then I will be better able to analyze my time-series data.

Many thanks and sorry for my confusion on this topic.