Pathway importance by graph convolutional network and Shapley additive explanations in gene expression phenotype of diffuse large B-cell lymphoma

Abstract

Deep learning techniques have recently been applied to analyze associations between gene expression data and disease phenotypes. However, there are concerns regarding the black box problem: it is difficult to interpret why the prediction results are obtained using deep learning models from model parameters. New methods have been proposed for interpreting deep learning model predictions but have not been applied to genetics. In this study, we demonstrated that applying SHapley Additive exPlanations (SHAP) to a deep learning model using graph convolutions of genetic pathways can provide pathway-level feature importance for classification prediction of diffuse large B-cell lymphoma (DLBCL) gene expression subtypes. Using Kyoto Encyclopedia of Genes and Genomes pathways, a graph convolutional network (GCN) model was implemented to construct graphs with nodes and edges. DLBCL datasets, including microarray gene expression data and clinical information on subtypes (germinal center B-cell-like type and activated B-cell-like type), were retrieved from the Gene Expression Omnibus to evaluate the model. The GCN model showed an accuracy of 0.914, precision of 0.948, recall of 0.868, and F1 score of 0.906 in analysis of the classification performance for the test datasets. The pathways with high feature importance by SHAP included highly enriched pathways in the gene set enrichment analysis. Moreover, a logistic regression model with explanatory variables of genes in pathways with high feature importance showed good performance in predicting DLBCL subtypes. In conclusion, our GCN model for classifying DLBCL subtypes is useful for interpreting important regulatory pathways that contribute to the prediction.

Enhancing patient stratification and interpretability through class-contrastive and feature attribution techniques

“…SHapley Additive exPlanations (SHAP) [4] is another state-of-the-art explainability technique. Fast approximations of SHAP have been applied to analyse gene expression data [7,8,9,10,11], such as kernelExplainer, treeExplainer and gradientExplainer [12]. Yu et al use a deep autoencoder [9] to learn gene expression representations, applying treeExplainer SHAP to measure the contributions of genes to each of the latent variables.…”

“…The ability to generate local explanations, for example, using waterfall plots, may be useful for applications in personalised medicine. Compared to state-of-the-art work, which tends to apply SHAP to black-box neural networks [7,8,9,10], we apply SHAP using a probabilistic model derived from a GMM that captures disease subtype relationships. This further improves the interpretability of SHAP values.…”

Deep learning for predicting 16S rRNA gene copy number

“…Deep learning models could provide precise trait predictions directly from www.nature.com/scientificreports/ biological sequence data. By evaluating the significance of each nucleotide or amino acid feature, these models can also be used to link specific loci to the traits of interest 69,70 .…”