Differential expression analysis of genes and isoforms is important in obtaining the function of genes and isoforms, thus becoming an essential research focus of bioinformatics. RNA-seq is a new experimental technique based on high-throughput sequencing and is increasingly used in transcriptome research. Read-isoform multi-mappings make it difficult to detect differential expression of isoforms. Here, we proposed a new method, called PG_bayes, to detect differential expression for both genes and isoforms. PG_bayes, based on expressions estimation method PGseq, uses a Bayes factor model selection method to detect differential expression. We applied PG_bayes to three human datasets and one mouse dataset, and compared its performance with popular alternatives. Results show that PG_bayes performs favorably in sensitivity and specificity at both gene and isoform levels.