How does DESeq2 calculate adjusted p-value?
Bonferroni: The adjusted p-value is calculated by: p-value * m (m = total number of tests). This is a very conservative approach with a high probability of false negatives, so is generally not recommended.
What are dispersion estimates?
Dispersion is a measure of spread or variability in the data. Variance, standard deviation, IQR, among other measures, can all be used to measure dispersion. However, DESeq2 uses a specific measure of dispersion (α) related to the mean (μ) and variance of the data: Var = μ + α*μ^2 .
What is adjusted p-value?
The adjusted P value is the smallest familywise significance level at which a particular comparison will be declared statistically significant as part of the multiple comparison testing. A separate adjusted P value is computed for each comparison in a family of comparisons.
When to use argument contrast in deseq2?
The argument contrast can be used to generate results tables for any comparison of interest, for example, the log2 fold change between two levels of a factor, and its usage is described below. It can also accomodate more complicated numeric comparisons.
Which is the default correction method in deseq2?
The default method for multiple test correction in DESeq2 is an implementation of the Benjamini-Hochberg false discovery rate (FDR). There are other corrections methods available and can be changed by adding the pAdjustMethod argument to the results () function. Let’s take a closer look at our results table.
How is the Wald test performed in deseq2?
It actually performs a statistical test against the desired threshold, by performing a two-tailed test for log2 fold changes greater than the absolute value specified. The user can change the alternative hypothesis using altHypothesis and perform two one-tailed tests as well.
How is deseq2 used to test Differential expression?
The package DESeq2 provides methods to test for differential expression by use of negative binomial generalized linear models; the estimates of dispersion and logarithmic fold changes incorporate data-driven prior distributions. This vignette explains the use of the package and demonstrates typical workflows.