Deseq2, multifactor model design for comparing adult Mutant mouse versus pup Mutant mouse with normalization to the age difference of the Wildtype condition.
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Last seen 9 weeks ago

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,


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"
DGE DESeq2 • 310 views
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and in case I used the interactive model :


> resultsNames(ddsObj)
[1] "Intercept"       "Status_TG_vs_WT" "Age_P14_vs_P0"   "StatusTG.AgeP14"

by taking the results of these two contasts "Age_P14_vs_P0" "StatusTG.AgeP14" will I be able to address the difference between P14 tg and P0 tg taking the age difference into consideration?

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swbarnes2 ★ 1.3k
Last seen 7 hours ago
San Diego

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)

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thank you for your reply and help, yes actually am aware when to use them and also my experiment is similar to the one stated in here DESeq2 - multifactorial design , and I tried the simplified design according to the comments written there in order to try. my problem is I couldn't be sure which contrasts can state my case which is p14tg vs p0tg taking only the effect of the mutation and not the gene difference that happen according to development between p14 vs p0 and this is why I added the age factor. 1- I wanted to make sure that the contrasts I have chose address my question. 2- I wanted to clarify about something in this experiment I have used 12 samples 3 for each case including wt and mut and both ages : I have tried earlier to do separately 6 samples including P14tg vs P0tg taking only the mutation as a factor ~Status and by comparing it to the experiment that contain 12 samples and taking the contrasts " Age_P14_vs_P0" "StatusTG.AgeP14" the log to fold change for some genes is still the same but the pvalue changed for some genes which is ok since am changing the factors and the model but now am confused which design is better to use for my case and most importantly the mutated gene which I have in overexpression become not visible with pvalue stated to NA which was very weird to me. so my other question was what could be the reason to have a gene p value to NA?


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