Abstract:

RNAseq technology has been widely applied in detecting differential gene and isoform expression. However, many methods have been developed for detecting difference in expression for each individual isoform of a gene, rather than for the ratio of all the isoforms in the same gene. Now we present a new method to test each gene for differential isoform ratio between two conditions. The method is based on the previously designed sLDASeq and adopts the KL divergence for the detection of differential isoform ratio. We first use the new benchmark, SEQC, to validate sLDASeq’s performance on gene and isoform expression calculation. The results show that the model can calculate the proportion of isoforms in a gene accurately. We then use the KL divergence of the probability of the latent variables of the sLDASeq to detect differential isoform ratios between the two conditions of simulation datasets. The results show that the proposed method has a high accuracy in comparison with other methods in detecting differential isoform ratio.

Key words: RNA-Seq, gene isoform expression, smoothed LDA, KL divergence, differential isoform ratio