Estimating differential expression with edgeR
The edgeR package in Bioconductor is a tool for identifying differentially expressed genes from RNA-Seq data. It offers a range of normalization methods for correcting differences in library size and sequencing depth, including the trimmed mean of M-values (TMM) method. TMM is a popular normalization method that uses the mean of log-transformed expression values to scale the data, taking into account the differences in library size between samples.
In addition to normalization, edgeR also provides a range of statistical models for testing differential expression. One of the main models used in edgeR is the negative binomial model, which is a type of generalized linear model (GLM) that is well-suited for modeling count data such as RNA-Seq data. The negative binomial model allows for the estimation of the mean and dispersion of the expression counts, and can account for overdispersion, which is common in RNA-Seq data. Overall, edgeR is...
