Limma uses a series of statistical techniques to identify differentially expressed genes. The process generally involves the following steps:
Data Preprocessing: This involves background correction, normalization, and summarization of the data. Linear Modeling: Limma fits a linear model to the expression data for each gene. This model accounts for various factors such as treatment groups, batches, and other covariates. Empirical Bayes Moderation: Limma applies an empirical Bayes method to moderate the standard errors of the estimated log-fold changes, improving the reliability of the results. Statistical Testing: Finally, statistical tests are performed to identify genes that are significantly differentially expressed between the conditions of interest.
