Cheng Liu

Accurate deconvolution of bulk and spatial transcriptomes is essential for studying tissue architecture and disease, yet remains challenged by unmodeled differences in cellular RNA content and cross-source heterogeneity. We introduce CSsingle, a unified deconvolution framework that explicitly corrects for cell-type-specific RNA content differences using either External RNA Controls Consortium (ERCC) spike-ins or a computational estimator, while robustly harmonizing data across platforms. CSsingle employs an iteratively reweighted least-squares model initialized by marker-gene sectional linearity, enabling accurate inference of cell-type proportions from diverse single-cell references. In bulk data, CSsingle outperforms existing methods by correcting systematic errors, including neutrophil underestimation in blood and tumor purity underestimation in breast tumor. Applied to spatial transcriptomics, CSsingle enables fine-grained mapping of cellular organization in the developing human pancreas and reveals functionally distinct niches in colon cancer. By integrating cell size awareness with cross-platform robustness, CSsingle advances the integrative analysis of complex tissues.

The development of single-cell RNA sequencing (scRNA-seq) technology has enabled the exploration of biological processes at the cellular level. A critical task in scRNA-seq data analysis is the unsupervised clustering of cells to distinguish different cell types. While various clustering methods have been successfully developed for scRNA-seq data, they still face limitations, particularly in terms of unstable clustering performance. This is often due to their inability to fully capture the intrinsic properties of cells, especially in the presence of high dropout rates and noise in the data. In this work, we propose a SEmantic-Aware contrastive Learning (SEAL) approach for scRNA-seq clustering. Specifically, we randomly mask the gene expression of each cell to generate two different augmentations of the cell data, and then apply semantic-aware contrastive learning to capture semantically invariant representations across these augmentations by leveraging semantic information from generated pseudo-labels. Experimental results demonstrate that our method effectively learns biologically meaningful representations and accurately identifies cell types.

Single-cell RNA sequencing (scRNA-seq) technology has transformed gene expression studies by enabling analysis at the individual cell level, offering unpre

Identifying cancer subtypes is crucial for understanding disease progression and guiding precision medicine. With advances in high-throughput experimental technologies, the integration of multiple types of omics data for cancer subtype identification has become increasingly feasible. However, despite the promising performance of existing integrative cancer subtyping methods, efficiently integrating and clustering multi-omics datasets remains challenging due to the high levels of noise inherent in omics data, which impede the accurate characterization of relationships among samples. To address these challenges, we propose a novel deep multi-view subspace clustering model that incorporates a self-reinforced learning strategy. This strategy iteratively improves the quality of self-representation, which is critical for accurately capturing sample relationships and enabling effective clustering. Specifically, during model training, the proposed method learns a highly reliable self-representation through a good-neighbor learning mechanism, allowing it to model more accurate and robust inter-sample relationships. Building upon this reliable self-representation, we further develop a learnable view-graph fusion framework that integrates complementary information across multiple omics views to derive a consensus representation for clustering, thereby guiding the overall learning process. In addition, we introduce a local graph-guided learning mechanism based on an initial graph constructed from the raw data. This mechanism serves as an effective regularization strategy to prevent the model from converging to suboptimal solutions, thereby enhancing stability and robustness during training. Extensive experimental results demonstrate that the proposed method consistently outperforms several state-of-the-art approaches, validating its effectiveness and robustness for cancer subtype identification.

Recently, neighbor-based contrastive learning has been introduced to effectively exploit neighborhood information for clustering. However, these methods rely on the homophily assumption-that connected nodes share similar class labels and should therefore be close in feature space-which fails to account for the varying homophily levels in real-world graphs. As a result, applying contrastive learning to low-homophily graphs may lead to indistinguishable node representations due to unreliable neighborhood information, making it challenging to identify trustworthy neighborhoods with varying homophily levels in graph clustering. To tackle this, we introduce a novel neighborhood Neutral Contrastive Graph Clustering method, NeuCGC, that extends traditional contrastive learning by incorporating neutral pairs-node pairs treated as weighted positive pairs, rather than strictly positive or negative. These neutral pairs are dynamically adjusted based on the graph's homophily level, enabling a more flexible and robust learning process. Leveraging neutral pairs in contrastive learning, our method incorporates two key components: (1) an adaptive contrastive neighborhood distribution alignment that adjusts based on the homophily level of the given attribute graph, ensuring effective alignment of neighborhood distributions, and (2) a contrastive neighborhood node feature consistency learning mechanism that leverages reliable neighborhood information from high-confidence graphs to learn robust node representations, mitigating the adverse effects of varying homophily levels and effectively exploiting highly trustworthy neighborhood information. Experimental results demonstrate the effectiveness and robustness of our approach, outperforming other state-of-the-art graph clustering methods. Our code is available at https://github.com/THPengL/NeuCGC.

Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in real-world scenarios, collecting strictly aligned views is challenging, and learning from both aligned and unaligned data becomes a more practical solution. Partially View-aligned Clustering aims to learn correspondences between misaligned view samples to better exploit the potential consistency and complementarity across views, including both aligned and unaligned data. However, most existing PVC methods fail to leverage unaligned data to capture the shared semantics among samples from the same cluster. Moreover, the inherent heterogeneity of multi-view data induces distributional shifts in representations, leading to inaccuracies in establishing meaningful correspondences between cross-view latent features and, consequently, impairing learning effectiveness. To address these challenges, we propose a Semantic MAtching contRasTive learning model (SMART) for PVC. The main idea of our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning to fully exploit semantic relationships in both aligned and unaligned data. Extensive experiments on eight benchmark datasets demonstrate that our method consistently outperforms existing approaches on the PVC problem.

Unsupervised cell type identification is crucial for uncovering and characterizing heterogeneous populations in single cell omics studies. Although a range of clustering methods have been developed, most focus exclusively on intrinsic cellular structure and ignore the pivotal role of cell-gene associations, which limits their ability to distinguish closely related cell types. To this end, we propose a Refinement Contrastive Learning framework (scRCL) that explicitly incorporates cell-gene interactions to derive more informative representations. Specifically, we introduce two contrastive distribution alignment components that reveal reliable intrinsic cellular structures by effectively exploiting cell-cell structural relationships. Additionally, we develop a refinement module that integrates gene-correlation structure learning to enhance cell embeddings by capturing underlying cell-gene associations. This module strengthens connections between cells and their associated genes, refining the representation learning to exploiting biologically meaningful relationships. Extensive experiments on several single-cell RNA-seq and spatial transcriptomics benchmark datasets demonstrate that our method consistently outperforms state-of-the-art baselines in cell-type identification accuracy. Moreover, downstream biological analyses confirm that the recovered cell populations exhibit coherent gene-expression signatures, further validating the biological relevance of our approach. The code is available at https://github.com/THPengL/scRCL.

Multi-view clustering has been empirically shown to improve learning performance by leveraging the inherent complementary information across multiple views of data. However, in real-world scenarios, collecting strictly aligned views is challenging, and learning from both aligned and unaligned data becomes a more practical solution. Partially View-aligned Clustering (PVC) aims to learn correspondences between misaligned view samples to better exploit the potential consistency and complementarity across views, including both aligned and unaligned data. However, most existing PVC methods fail to leverage unaligned data to capture the shared semantics among samples from the same cluster. Moreover, the inherent heterogeneity of multi-view data induces distributional shifts in representations, leading to inaccuracies in establishing meaningful correspondences between cross-view latent features and, consequently, impairing learning effectiveness. To address these challenges, we propose a Semantic MAtching contRasTive learning model (SMART) for PVC. The main idea of our approach is to alleviate the influence of cross-view distributional shifts, thereby facilitating semantic matching contrastive learning to fully exploit semantic relationships in both aligned and unaligned data. Specifically, we mitigate view distribution shifts by aligning cross-view covariance matrices, which enables the inference of a semantic graph for all data. Guided by the learned semantic graph, we further exploit semantic consistency across views through semantic matching contrastive learning. After the optimization of the above mechanisms, our model smoothly performs semantic matching for different view embeddings instead of the cumbersome view realignment, which enables the learned representations to enjoy richer category-level semantics and stronger robustness. Extensive experiments on eight benchmark datasets demonstrate that our method consistently outperforms existing approaches on the PVC problem. The code is available at https://github.com/THPengL/SMART.