Identifying cancer subtypes is crucial for understanding disease progression. With advancements in high-throughput experimental technology, leveraging multiple types of omic data for subtype identification has become feasible. Various integrative cancer subtyping methods present a promising computational approach for identifying cancer subtypes from heterogeneous datasets. While existing integrative cancer subtyping methods have shown promising results in this task, efficiently integrating and clustering multi-omics datasets remains challenging due to high noise levels in omics data, which hinder accurate relationship capture among samples. To overcome this challenge, we propose a new deep multi-view subspace clustering model that introduces a self-reinforced learning strategy. This strategy iteratively enhances the quality of self-representation, crucial for capturing relationships among samples and for clustering. Specifically, during model training, our method is capable of learning a highly reliable self-representation by leveraging a good neighbor learning approach. This capability enables us to capture more accurate and robust relationships among samples. Subsequently, with the assistance of this highly reliable self-representation, we further develop a learnable view-graph fusion approach, which enables us to learn an accurate consensus for clustering and guides the overall model learning process. Additionally, we introduce a local graph-guided learning mechanism based on an initial graph learned from raw data. This mechanism helps prevent the model from converging to suboptimal solutions, thereby avoiding unsatisfactory and unstable results. Experimental results demonstrate that our method outperforms several state-of-the-art methods, verify the effectiveness of our approach in cancer subtype identification task.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) enables high-throughput transcriptomic profiling at single-cell resolution. The inherent spatial location is crucial for understanding how single cells orchestrate multicellular functions and drive diseases. However, spatial information is often lost during tissue dissociation. Spatial transcriptomic (ST) technologies can provide precise spatial gene expression atlas, while their practicality is constrained by the number of genes they can assay or the associated costs at a larger scale and the fine-grained cell-type annotation. By transferring knowledge between scRNA-seq and ST data through cell correspondence learning, it is possible to recover the spatial properties inherent in scRNA-seq datasets. RESULTS: In this study, we introduce COME, a COntrastive Mapping lEarning approach that learns mapping between ST and scRNA-seq data to recover the spatial information of scRNA-seq data. Extensive experiments demonstrate that the proposed COME method effectively captures precise cell-spot relationships and outperforms previous methods in recovering spatial location for scRNA-seq data. More importantly, our method is capable of precisely identifying biologically meaningful information within the data, such as the spatial structure of missing genes, spatial hierarchical patterns, and the cell-type compositions for each spot. These results indicate that the proposed COME method can help to understand the heterogeneity and activities among cells within tissue environments. AVAILABILITY AND IMPLEMENTATION: The COME is freely available in GitHub (https://github.com/cindyway/COME).

Partially View-aligned Clustering (PVC) presents a challenge as it requires a comprehensive exploration of complementary and consistent information in the presence of partial alignment of view data. Existing PVC methods typically learn view correspondence based on latent features that are expected to contain common semantic information. However, latent features obtained from heterogeneous spaces, along with the enforcement of alignment into the same feature dimension, can introduce cross-view discrepancies. In particular, partially view-aligned data lacks sufficient shared correspondences for the critical common semantic feature learning, resulting in inaccuracies in establishing meaningful correspondences between latent features across different views. While feature representations may differ across views, instance relationships within each view could potentially encode consistent common semantics across views. Motivated by this, our aim is to learn view correspondence based on graph distribution metrics that capture semantic view-invariant instance relationships. To achieve this, we utilize similarity graphs to depict instance relationships and learn view correspondence by aligning semantic similarity graphs through optimal transport with graph distribution. This facilitates the precise learning of view alignments, even in the presence of heterogeneous view-specific feature distortions. Furthermore, leveraging well-established cross-view correspondence, we introduce a cross-view contrastive learning to learn semantic features by exploiting consistency information. The resulting meaningful semantic features effectively isolate shared latent patterns, avoiding the inclusion of irrelevant private information. We conduct extensive experiments on several real datasets, demonstrating the effectiveness of our proposed method for the PVC task.