While limma is a powerful tool, it has some limitations:
Assumption of Linear Models: Limma assumes that the relationship between gene expression and the factors of interest is linear, which may not always be the case. Requirement of Good Quality Data: The accuracy of limma's results depends on the quality of the input data. Poor quality data can lead to misleading conclusions. Complexity: Although limma is flexible, setting up the correct model for complex experimental designs can be challenging.
