A fair experimental comparison of neural network architectures for latent representations of multi-omics for drug response prediction

Abstract

Background: Recent years have seen a surge of novel neural network architectures for the integration of multi-omics data for prediction. Most of the architectures include either encoders alone or encoders and decoders, i.e., autoencoders of various sorts, to transform multi-omics data into latent representations. One important parameter is the depth of integration: the point at which the latent representations are computed or merged, which can be either early, intermediate, or late. The literature on integration methods is growing steadily, however, close to nothing is known about the relative performance of these methods under fair experimental conditions and under consideration of different use cases.

Results: We developed a comparison framework that trains and optimizes multi-omics integration methods under equal conditions. We incorporated early integration, PCA and four recently published deep learning methods: MOLI, Super.FELT, OmiEmbed, and MOMA. Further, we devised a novel method, Omics Stacking, that combines the advantages of intermediate and late integration. Experiments were conducted on a public drug response data set with multiple omics data (somatic point mutations, somatic copy number profiles and gene expression profiles) that was obtained from cell lines, patient-derived xenografts, and patient samples. Our experiments confirmed that early integration has the lowest predictive performance. Overall, architectures that integrate triplet loss achieved the best results. Statistical differences can, overall, rarely be observed, however, in terms of the average ranks of methods, Super.FELT is consistently performing best in a cross-validation setting and Omics Stacking best in an external test set setting.

Conclusions: We recommend researchers to follow fair comparison protocols, as suggested in the paper. When faced with a new data set, Super.FELT is a good option in the cross-validation setting as well as Omics Stacking in the external test set setting. Statistical significances are hardly observable, despite trends in the algorithms' rankings. Future work on refined methods for transfer learning tailored for this domain may improve the situation for external test sets. The source code of all experiments is available under https://github.com/kramerlab/Multi-Omics_analysis.

Keywords: Autoencoder; Deep learning; Drug response; Machine learning; Multi-omics; Neural network.

Fig. 1

Mean rank and critical difference of the AUROC and AUPRC on the test sets from cross-validation. The mean values of the outer cross-validation results are compared. The Nemenyi test with $\alpha =0.05$ was used to compute significant differences

Fig. 2

Mean rank and critical difference of the AUROC and AUPRC on the external test set. The mean values of the outer cross-validation results are compared. The Nemenyi test with $\alpha =0.05$ was used to compute significant differences

Fig. 3

Mean rank and critical difference of the AUROC and AUPRC on the test sets from cross-validation for the ablation study. The mean values of the outer cross-validation results are compared. The Nemenyi test with $\alpha =0.05$ was used to compute significant differences

Fig. 4

Mean rank and critical difference of the AUROC and AUPRC on the external test data for the ablation study. The mean values of the outer cross-validation results are compared. The Nemenyi test with $\alpha =0.05$ was used to compute significant differences

Fig. 5

Schematic architecture of early integration with three input omics

Fig. 6

Schematic architecture of PCA integration with three input omics

Fig. 7

Schematic architecture of MOLI with three input omics

Fig. 8

Schematic architecture of Super.FELT with three input omics

Fig. 9

Schematic architecture of Omics-Stacking with three input omics

Fig. 10

Schematic architecture of the network used in MOMA for two input omics

Fig. 11

Schematic architecture of OmiEmbed for three input omics