Hi, I am new to RNASeq and DESeq2 and I am trying to do the analysis. My experiment has three groups (Control: X-overexpression= Disease: X+Y-overexpression= Treatment) with two samples in each. I did DESeq2 with RNASeq data to find the differential expression between two groups (pair-wise comparison: Disease vs. Control and Treatment vs. Disease). I have provided the code. However, I have been suggested to use a single model matrix (y ~ Disease+Treatment) to simultaneously evaluate the effect of X-overexpression and X+Y-overexpression using all samples. Genes with β(X) <> 0 are X-induced dysregulated genes. Genes with β(X+Y) <> 0 and has an opposite sign of β(X) are Y-rescued genes. I do not understand what is "single model matrix". I will be thankful if anyone directs me on this.

```
# setting the metadata for the samples
sample <- c('C1', 'C2', 'D1','D2', 'T1', 'T2')
condition <- c(rep('Control', 2), rep('Disease', 2), rep('Treatment', 2))
metadata <- data.frame(sample, condition)
metadata <- column_to_rownames(metadata, 'sample')
# sample order
all(colnames(file) == rownames(metadata)
# Create DESeqDataSet object
dds <- DESeqDataSetFromMatrix(countData = file,
colData = metadata,
design = ~condition)
# Differential Expression Analysis
dds <- DESeq(dds)
# Building the results table
(res_A_W <- results(dds,
contrast = c('condition', 'Disease', 'Control'),
alpha = 0.05))
(res_AT_A <- results(dds,
contrast = c('condition', 'Treatment', 'Disease'),
alpha = 0.05))
```

I went through the "multi-factor designs" section of the DESeq2 vignette. It suggests two variables (~type+condition) where each level of variable 1 has every level of variable 2. However, my design has only one variable (Genotype and condition are the same, I have edited the code). My treatments, X (Disease) and X+Y (treatment) are the levels of condition variable but not as variables itself to use in the design formula. I will be thankful if you let me know if my understanding is correct. Regards Bhanu

If I am understanding your experimental design, it appears you set it up correctly at first. You can only add more variables to the regression formula if each sample has more than one factor level. For example, if you had samples such that you have controls with and without a treatment, and disease with and without a treatment, each sample would have a two factor design (

`~Disease + Treatment`

).Yes, what you said is correct. The two-factor design is analogous to two-way ANOVA. I have posted the same on the Bioconductor support site and I got a similar response. Thank you, @rpolicastro.