Most predictive models based on gene expression data do not leverage information related to gene splicing, despite the fact that splicing is a fundamental feature of eukaryotic gene expression. Cigarette smoking is an important environmental risk factor for many diseases, and it has profound effects on gene expression. Using smoking status as a prediction target, researchers from Northeastern University and Brigham and Women’s Hospital developed deep neural network predictive models using gene, exon, and isoform level quantifications from RNA sequencing data in 2,557 subjects in the COPDGene Study. The researchers observed that models using exon and isoform quantifications clearly outperformed gene-level models when using data from 5 genes from a previously published prediction model. Whereas the test set performance of the previously published model was 0.82 in the original publication, the exon-based models including an exon-to-isoform mapping layer achieved a test set AUC (area under the receiver operating characteristic) of 0.88, which improved to an AUC of 0.94 using exon quantifications from a larger set of genes. Isoform variability is an important source of latent information in RNA-seq data that can be used to improve clinical prediction models.
(a) Dataset split and usage. The number in each cell represents the number of subjects. The training set is equally split into 5 folds for deep learning model optimization (cross-validation for tuning the hyperparameters and architecture search in a deep learning model). The validation set is used to select the optimal model and the testing set is held out for performance evaluation. (b) Model overview. This model consists of a Feature Selection Layer (FSL), an Isoform Map Layer (IML) (if the input feature is exon) and standard fully connected layers. FSL associates each input feature with a non-negative learnable weight, which represents the importance of features with respect to smoking status. IML encodes exon to isoform relationships via a binary matrix R, such that if exon i is contained within isoform j, we set Rij = 1, otherwise Rij = 0. By (element-wise) multiplying Rij with corresponding learnable weights W, we only consider canonical exon to isoform relationships.