**1.2k**wrote:

Hi All,
I am working on RNAseq data analysis using DESeq2 R package.
I havemy code `dds <- DESeqDataSetFromMatrix(countData = count.mat , colData = cond, design = ~Strain + Time)`

to create the matrix.
My confusion is with the design formula `design = ~Strain + Time`

where I have `Strain`

and `Time`

variables to compare in my colData for my countData matrix.

This is my `cond`

matrix

```
Strain Time
count1 1 1
count2 1 1
count4 1 2
count5 1 2
count13 2 1
count14 2 1
count16 2 2
count17 2 2
countX 2 3
```

First,
I want to understand the difference between these four designs that could go in the function `DESeqDataSetFromMatrix`

:

```
a) `design = ~Strain + Time + Strain:Time`
b) `design = ~Strain + Time`
c) `design = ~Time`
and d) `design = ~Strain`
```

Second,

My understanding is that the DESeq2 takes the last variable in the design formula (here Time) as a control variable, so to test for different samples in Time group, I have these codes below. So, I want to know what the outputs of `resultNames(ddsTC)`

really mean?

```
ddsTC <- DESeqDataSet(dds, ~ Strain + Time ) ##For time
ddsTC <- DESeq(ddsTC, test="LRT", reduced = ~Time ) #For Time
resultsNames(ddsTC)
[1] "Intercept.1" "Time_3_vs_1.1" "Time_2_vs_1.1" "Strain_2_vs_1.1"
```

**80k**• written 8 months ago by MAPK •

**1.2k**

Hey, To the best of my knockledge A)

`design = ~Strain + Time`

means that deseq2 will test the effect of the Time (the last factor), controlling for the effect of Strain (the first factor), so that the algorithm returns the fold change result only from the effect of time. B)`design = ~Time`

here the algorithm will return the fold change that result from time without correcting for fold change that result from strain C)`design = ~Strain`

same as aboveSo in my understanding Deseq2 treats the first factor as a co-variate and tries to eliminate the fold change that result because of this co-variate.

140Thank you so much for your answer. So what does

`design = ~Strain + Time + Strain:Time`

mean? Also, do you know what are the outputs of`resultsNames(ddsTC)`

comparing?1.2kRegarding 'design = ~Strain + Time + Strain:Time` ,

Here you added an interaction term (how time is interacting with stain in relation to regulation of gene expression). So this design will return the effect of time on the reference level of strain (1 or 2 depends on your setting). Using contrast () you can look for the effect of time on the other level in "strain"

Alternatively you can group the strain (with its different levels) and time ((with its different levels) into one factor, lets call it ALL. and by using contrast () you can look for the difference in log2 fold change between any combination of levels.

140