Detecting differential transcript usage across multiple conditions for RNA-seq data based on the smoothed LDA model

Jing Li, Xuejun Liu, Daoqiang Zhang

Abstract: In this paper, we propose a statistical framework MLDA, based on the smoothed LDA to detect the differential usage of transcript isoforms for RNA-seq data. The experimental results show that our method performs competitively on the detection of DTU and obtain more accurate relative transcript abundance compared with other alternatives on both simulated and real data.