Hello everyone, I am a beginner with the DESeq2 package, and I am trying to understand the mathematical principles behind this package.

In statistical modeling and hypothesis testing, a contrast vector c is used to construct specific linear combinations in order to test whether this linear combination equals a certain hypothesized value. A example below.

If I have a condition with four group, to avoid dummy trap & not full rank of matrix, a regression equation must like : Y = Beta_0 + Beta_1 X_1 + Beta_2 X_2 + Beta_3 * X_3 + error. Where "Y" is read counts and "Beta_1 to Beta_3" is dummy variables.

Question1: When I have a factor that has been incorporated into the intercept, and I want to compare Beta_1 with the factor that has been incorporated into the intercept, how should I set the contrast vector? Which one of the contrast vector ( [1,-1,0,0] or [0,1,0,0] ) is correct?

Question2: Log2foldChange represents the logarithmic expression difference between two groups. When the contrast vector multiply Beta vector = q. Is set to q = 0, doesn't taking the logarithm result in infinity? How does the software avoid the occurrence of infinite values?

If your coefficients are the contrasts you want, you can just use the name argument. Or you can specify the contrast using the column and levels from your colData, or you can specify the contrast using your second option. Or if the coefficient you care about is the last one, you can just use the defaults.

>  dds <- makeExampleDESeqDataSet(m=4)
>  dds <- DESeq(dds)
estimating size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
> first <- results(dds, name = "condition_B_vs_A")
> second <- results(dds)
> third <- results(dds, c("condition","B","A"))
> fourth <- results(dds, c(0,1))
> all.equal(first, second)
[1] TRUE
> all.equal(first, third)
[1] TRUE
> all.equal(first, fourth, check.attributes = FALSE)
[1] TRUE