深層学習フレームワーク下での単一細胞マルチオミクスデータの可視化：NeuroMDAVIS

Single-cell technologies have favored extensive advancements in cell-type discovery, cell state identification, development of lineage tracing, and disease understanding among others. Further, single-cell multi-omics data generated using modern technologies provide several views of omics contribution for the same set of cells. Analyzing these views of multi-omics data is hindered by large dimensions of the same. In this regard, one effective approach is dimensionality reduction and thereby visualization (in 2D or 3D space) of the integrated views of multi-omics data. However, dimension reduction and visualization of these datasets remain a challenging task since obtaining a low-dimensional embedding that preserves information about local and global structures in data is difficult. Moreover, combining different views obtained from each omics layer to interpret the underlying biology is even more challenging. In this work, we introduce NeuroMDAVIS, a novel unsupervised deep neural network model, for joint visualization of biological datasets having multiple modalities. Joint visualization refers to transforming the feature space of each modality and combining them to produce a latent embedding that supports visualization of the multi-modal dataset in the newly transformed feature space. NeuroMDAVIS transforms the feature space of each modality and integrates them into a shared latent space, capturing both modality-specific and common information across different layers. The model effectively learns both local and global relationships within the data, providing a meaningful low-dimensional representation for further analysis. NeuroMDAVIS is able to capture both individual modality-specific information as well as common information across all modalities. When it comes to visualization capability, NeuroMDAVIS competes against state-of-the-art visualization models such as t-distributed Stochastic Neighbor Embedding (t-SNE), Uniform Manifold Approximation and Projection (UMAP), Fast interpolation-based t-SNE (Fit-SNE), the Siamese network-based visualization method (IVIS), and the manifold learning-based generalized version of UMAP, called MultiMAP. Downstream analyses have reflected effective classification and clustering performance over all the datasets, in terms of accuracy, precision, recall, F1 score, and various cluster validity indices. To the best of our knowledge, NeuroMDAVIS is the first model to offer joint visualization for multi-modal biological datasets. It competes with the state-of-the-art visualization methods, providing a robust and efficient approach for understanding complex multi-omics data.