Hello! I would appreciate the help with modeling design of My bulk RNA experiment, as I have 4 groups (pups at day 0 and 14 both with mutation and control) P0.WT, P0.TG, P14WT, P14.TG after checking the differences of each age group separately it was interesting to check the difference between P14.TG vs P0.TG taking into account the differences that happen normally with age during development between P0.WT and P14.WT, I tried different models between additive and interaction; using age, genotype factors like (Model: ~genotype+ Age), (Model: ~genotype +Age+ genotype: Age) respectively, in the end I considered simplifying the model by having one status combining age and genotype like this (Status: P0.WT, P0.TG, P14WT, P14.TG) and using (model:~Status). unfortunately I didn't get the contrast am looking for both cases of (P14.TG vs P0 TG) and (P14WT vs P0WT) (provided by the code) to be honest I was also confused by the intercept here and what it might be.. My question is the following: is there a way in this analysis to retrieve these two contrasts P14.TG vs P0 TG) and (P14WT vs P0WT) and if not I can do the analysis separately again for each group but is there a way for normalizing the development effect (age diferences) between P14wt vs P0wt and get only results showing differences how this mutation affected these ages?

kind regards,

Ruba

simple.model <- as.formula(~Status)
model.matrix(simple.model,data = sampleinfo)

  (Intercept) StatusWT.P14 StatusTG.P0 StatusTG.P14
1            1            0           0            0
2            1            0           0            0
3            1            0           0            0
4            1            0           1            0
5            1            0           1            0
6            1            0           1            0
7            1            1           0            0
8            1            1           0            0
9            1            1           0            0
10           1            0           0            1
11           1            0           0            1
12           1            0           0            1

> resultsNames(ddsObj)
[1] "Intercept"              "Status_WT.P14_vs_WT.P0" "Status_TG.P0_vs_WT.P0"  "Status_TG.P14_vs_WT.P0"

The Status grouping you show above is good to use if you want to simply compare one subset of samples to another subset.

The ~Genotype + age + genotype:age design will let you find genes where the gene change over time is different between the genotypes. (Like, the gene drops 50% over time in the WT, but 80% in the other genotype)