bio-de-edger-basics

Identify differentially expressed genes from RNA-seq count data by constructing a DGEList, filtering low-expression genes, normalizing with TMM, and fitting a quasi-likelihood GLM. This framework extracts top DE genes while controlling FDR across condition groups.

Batch effects are handled during differential expression analysis by specifying batch variables within the design matrix. The edgeR quasi-likelihood framework uses this multi-factor design to estimate dispersion and isolate condition-associated gene expression changes accurately.

The quasi-likelihood framework in edgeR is used for robust differential expression testing. It estimates biological dispersion and fits generalized linear models to RNA-seq count data, ensuring accurate statistical testing and FDR control for complex multi-factor experiments.

Normalize RNA-seq counts and filter low-expression genes by constructing a DGEList from raw counts, removing genes below expression thresholds, and applying TMM normalization. This preprocessing ensures valid dispersion estimation before fitting quasi-likelihood models.

This edgeR analysis supports multi-factor experimental designs through flexible design matrix construction. It accommodates complex experiments, enabling simultaneous batch adjustment and condition comparison to identify differentially expressed genes with FDR control.

TMM normalization corrects for library size composition biases in RNA-seq count data, ensuring accurate dispersion estimation. FDR control adjusts p-values from quasi-likelihood testing to limit false positives in the final differentially expressed gene lists.