Spatial transcriptomics enables gene expression profiling with spatial context, offering unprecedented insights into the tissue microenvironment. However, most computational models treat genes as isolated numerical features, ignoring the rich biological semantics encoded in their symbols. This prevents a truly deep understanding of critical biological characteristics. To overcome this limitation, we present SemST, a semantic-guided deep learning framework for spatial transcriptomics data clustering. SemST leverages Large Language Models (LLMs) to enable genes to "speak" through their symbolic meanings, transforming gene sets within each tissue spot into biologically informed embeddings. These embeddings are then fused with the spatial neighborhood relationships captured by Graph Neural Networks (GNNs), achieving a coherent integration of biological function and spatial structure. We further introduce the Fine-grained Semantic Modulation (FSM) module to optimally exploit these biological priors. The FSM module learns spot-specific affine transformations that empower the semantic embeddings to perform an element-wise calibration of the spatial features, thus dynamically injecting high-order biological knowledge into the spatial context. Extensive experiments on public spatial transcriptomics datasets show that SemST achieves state-of-the-art clustering performance. Crucially, the FSM module exhibits plug-and-play versatility, consistently improving the performance when integrated into other baseline methods.

Spatially resolved transcriptomics (SRT) facilitates the simultaneous acquisition of gene expression profiles, spatial location, and histology images for spatial clustering analysis, providing transformative insights into cellular interactions and the underlying mechanisms of disease progression. Despite the success of existing research in spatial clustering tasks, most methods overlook the information imbalance arising among spots in intra- and inter-modal communication due to insufficient sequencing depth and modality discrepancies. To this end, we propose a novel multi-view information rebalance learning method for SRT data clustering, referred to as MIRL. Specifically, we construct hypergraphs for the gene and histological image modalities and leverage hypergraph neural networks to learn the hypergraph features, which helps mitigate the propagation of intra-modal information imbalance by capturing higher-order interactions among multiple spots, rather than relying solely on pairwise relationships in traditional feature graphs. To enhance the global coordination among spots and the interrelations between features across modalities, we perform intra-modal adaptive fusion of modality-specific hypergraph features and spatial features, followed by cross-modal integration. Furthermore, adaptive reconstruction of the cross-modal heterogeneous graph is employed to rebalance inter-modal information flow associated with pseudo-labels, ensuring more reliable information extraction by alleviating the impact of incorrect heterogeneous negative edges connections through the construction of hypergraph edges. Extensive experimental results demonstrate that the proposed MIRL achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Spatial transcriptomics, comprising spatial location and high-throughput gene expression information, provides revolutionary insights into disease discovery and cellular evolution. Spatial transcriptomic clustering, which pinpoints distinct spatial domains within tissues, reveals cellular interactions and enhances our understanding of the intricate architecture of tissues. Existing methods typically construct spatial graphs using a static radius based on spatial coordinates, which hinders the accurate identification of spatial domains and complicates the precise partitioning of boundary nodes within clusters. To address this issue, we introduce a novel spatially resolved transcriptomics data clustering network (TSstc). Specifically, we employ a tailored spatial-scale modulation approach, constructing different spatial graphs incrementally as the radius of the spatial domain expands, and a Spatiality-Aware Sampling (SAS) strategy is proposed to aggregate node representations by considering the spatial dependencies between spots. We then use GCN encoders to learn gene embedding with gene graph and multiple spatial embeddings with spatial graphs. During training, we incorporate cross-view correlation-based tailored spatial regularization constraints to preserve high-quality neighbor relationships across spatial embeddings at different scales. Finally, a zero-inflated negative binomial model is utilized to capture the global probability distribution of gene expression profiles. Extensive experimental results demonstrate that our approach surpasses existing state-of-the-art methods in clustering tasks and related downstream applications.

Spatial transcriptomics technology fully leverages spatial location and gene expression information for spatial clustering tasks. However, existing spatial clustering methods primarily concentrate on utilizing the complementary features between spatial and gene expression information, while overlooking the discriminative features during the integration process. Consequently, the discriminative capability of node representation in the gene expression features is limited. Besides, most existing methods lack a flexible combination mechanism to adaptively integrate spatial and gene expression information. To this end, we propose an end-to-end deep learning method named MAFN for spatially resolved transcriptomics data clustering via a multi-view adaptive fusion network. Specifically, we first adaptively learn inter-view complementary features from spatial and gene expression information. To improve the discriminative capability of gene expression nodes by utilizing spatial information, we employ two GCN encoders to learn intra-view specific features and design a Cross-view Correlation Reduction (CCR) strategy to filter the irrelevant information. Moreover, considering the distinct characteristics of each view, a Cross-view Attention Module (CAM) is utilized to adaptively fuse the multi-view features. Extensive experimental results demonstrate that the proposed MAFN achieves competitive performance in spatial domain identification compared to other state-of-the-art ones.

Home care (HC) services represent an effective solution to face the health issues related to population aging. However, several scheduling problems arise in HC, and the providers must make several scheduling and routing decisions, e.g., the assignment of caregivers to clients, in order to balance operating costs and client satisfaction. Starting from the analysis of a real HC provider operating in New York City, NY, USA, this article addresses a scheduling problem with chargeable overtime and preference matching and formulates it as an integer programming model. The objective is to minimize a cost function that includes traveling costs, the overtime cost paid by the provider, the preference mismatch, and a penalty related to the continuity of care violation. To solve this problem, we design a matheuristic algorithm that integrates a specific variable neighborhood search with a set covering model. The results demonstrate the applicability and efficiency of our approach to solving real-size instances. Sensitivity analyses are also performed to discuss practical insights. Note to Practitioners-This article provides a decision support tool to HC managers, which appropriately assigns caregivers to clients and makes routing decisions over a long horizon. Chargeable overtimes and preference matching enclosed in this tool are rarely considered in the literature, despite matching is relevant in HC caregiver-to-client assignments and chargeable overtime has potential in tailoring the service level based on the specific client. We formulate the scheduling problem as a mathematical model. Then, we propose a matheuristic algorithm to efficiently solve the problem in real-size instances. The results show the applicability and efficiency of our method. Thus, HC managers can exploit it to efficiently make assignment and routing decisions and to analyze the impact on other operating costs when adjusting any of them.