APAdeg enhances differentially expressed gene inference by leveraging site-specific signals in APA-seq data.

Alternative polyadenylation (APA) is a key post-transcriptional regulatory mechanism implicated in various diseases. Existing APA analysis tools are generally restricted to site detection and comparison, precluding differently expressed gene (DEG) analysis. Furthermore, standard RNA-seq-based DEG methods, though commonly used for gene expression profiling, demonstrate limited efficacy in identifying DEGs when directly applied to APA-seq datasets. To address this limitation, we developed APAdeg, a novel statistical method specifically tailored for DEG analysis of APA-seq data. APAdeg integrates both the total read count of a gene and the site-specific read counts within the gene into a generalized linear mixed model, thereby improving the efficiency of DEG detection. Benchmarking analyses on both simulated and empirical APA-seq data demonstrated that APAdeg consistently outperforms RNA-seq-based methods in DEG inference. Application of APAdeg to APA-seq data from distinct cancer types revealed that only a small proportion of DEGs exhibited significant changes in 3' untranslated region length, with an equally small proportion showing significant alterations in intronic APA usage. To facilitate widespread adoption, we implemented APAdeg as an R package. Collectively, APAdeg significantly enhances the accuracy of DEG analysis from APA-seq data, thereby advancing research into APA-mediated gene regulation in diseases and health.