Global and cross-modal feature aggregation for multi-omics data classification and application on drug response prediction

With rapid development of single-cell multi-modal sequencing technologies, more and more multi-omics data come into being and provide a unique opportunity for the identification of distinct cell types at the single-cell level. Therefore, it is important to integrate different modalities which are with high-dimensional features for boosting final multi-omics data classification performance. However, existing multi-omics data classification methods mainly focus on exploiting the complementary information of different modalities, while ignoring the learning confidence and cross-modal sample relationship during information fusion. In this paper, we propose a multi-omics data classification network via global and cross-modal feature aggregation, referred to as GCFANet. On one hand, considering that a large number of feature dimensions in different modalities could not contribute to final classification performance but disturb the discriminability of different samples, we propose a feature confidence learning mechanism to suppress some redundant features, as well as enhancing the expression of discriminative feature dimensions in each modality. On the other hand, in order to capture the inherent sample structure information implied in each modality, we design a graph convolutional network branch to learn the corresponding structure preserved feature representation. Then the modal-specific feature representations are concatenated and input to a transformer induced global and cross-modal feature aggregation module for learning consensus feature representation from different modalities. In addition, the consensus feature representation used for final classification is enhanced via a view-specific consistency preserved contrastive learning strategy. Extensive experiments on four multi-omics datasets are conducted to demonstrate the efficacy of the proposed GCFANet.