Recent advances in spatial transcriptomics have enabled subcellular-resolution profiling of gene expression, offering unprecedented opportunities to investigate intracellular architecture and local microenvironmental interactions. Graph neural networks (GNNs) have shown great promise in modeling spatial transcriptomics data. However, existing GNN-based methods primarily focus on low-frequency signals, overlooking high-frequency signals critical for resolving transcriptional differences across subcellular compartments and cell boundaries. This limits their ability to characterize fine-grained structural and functional heterogeneity within tissues, hindering accurate spatial domain identification. In this study, we propose HiFi-ST, a High-Frequency-Aware Graph Integration framework for subcellular spatial transcriptomics. HiFi-ST employs a high-pass filter to extract high-frequency transcriptional differences, which are then integrated with spatial contexts through a transformer-based architecture. A contrastive learning module is designed to enhance cell representation by aligning spatial organization with transcriptional heterogeneity. Comprehensive experiments on subcellular datasets demonstrated that HiFi-ST consistently outperformed six state-of-the-art methods in spatial clustering, gene expression enhancement, and niche identification.

Recent advances in spatial omics technologies have allowed various omics data to be obtained from a single tissue section. To fully explore the relationships among these different types of omics data, it is urgent to develop more effective methods for spatial multi-omics data integration. In this work, we propose a novel Multi-level Graph Contrastive Learning framework, named SMMGCL, to simultaneously mine complementary information at both spot and graph levels for integrating Spatial Multi-omics data. Specifically, to adaptively fuse multi-omics modalities, we first design a multi-modality autoencoder that integrates spatial locations with spot omic expressions to extract modality-specific embeddings. These embeddings are then fused into a consensus representation using an attention mechanism to capture spot-level cross-omics representations. Next, to explore the complex inter-omic structural information, we connect corresponding spots across different omics adjacency graphs into a heterogeneous graph. We then employ a graph convolutional network (GCN) to extract spatial correlations across the omics, learning a graph-level cross-omics global representation. Finally, SMMGCL aligns feature similarity matrixes between spot-level and graph-level representations with their pseudo-label similarity matrix, ensuring multi-level clustering consistency and leading to more accurate spatial multi-omics integration. Experimental results on simulated and real datasets from across tissues show that SMMGCL consistently outperforms other state-of-the-art methods in spatial multi-omics integration performance. The code for SMMGCL is available for download from the GitHub repository at https://github.com/cs-wangbo/SMMGCL.

MOTIVATION: Recent advances in spatial transcriptomics technologies have provided multi-modality data integrating gene expression, spatial context, and histological images. Accurately identifying spatial domains and spatially variable genes is crucial for understanding tissue structures and biological functions. However, effectively combining multi-modality data to identify spatial domains and determining SVGs closely related to these spatial domains remains a challenge. RESULTS: In this study, we propose spatial transcriptomics multi-modality and multi-granularity collaborative learning (spaMMCL). For detecting spatial domains, spaMMCL mitigates the adverse effects of modality bias by masking portions of gene expression data, integrates gene and image features using a shared graph convolutional network, and employs graph self-supervised learning to deal with noise from feature fusion. Simultaneously, based on the identified spatial domains, spaMMCL integrates various strategies to detect potential SVGs at different granularities, enhancing their reliability and biological significance. Experimental results demonstrate that spaMMCL substantially improves the identification of spatial domains and SVGs. AVAILABILITY AND IMPLEMENTATION: The code and data of spaMMCL are available on Github: Https://github.com/liangxiao-cs/spaMMCL.

Clustering cells into subgroups plays a critical role in single cell-based analyses, which facilitates to reveal cell heterogeneity and diversity. Due to the ever-increasing scRNA-seq data and low RNA capture rate, it has become challenging to cluster high-dimensional and sparse scRNA-seq data. In this study, we propose a single-cell Multi-Constraint deep soft K-means Clustering(scMCKC) framework. Based on zero-inflated negative binomial (ZINB) model-based autoencoder, scMCKC constructs a novel cell-level compactness constraint by considering association between similar cell, to emphasize the compactness between clusters. Besides, scMCKC utilizes pairwise constraint encoded by prior information to guide clustering. Meanwhile, a weighted soft K-means algorithm is leveraged to determine the cell populations, which assigns the label based on affinity between data and clustering center. Experiments on eleven scRNA-seq datasets demonstrate that scMCKC is superior to the state-of-the-art methods and notably improves cluster performance. Moreover, we validate the robustness on human kidney dataset, which demonstrates that scMCKC exhibits comprehensively excellent performance on clustering analysis. The ablation study on eleven datasets proves that the novel cell-level compactness constraint is conductive to the clustering results.

single-cell RNA-sequencing (scRNA-seq) technology can reveal cellular heterogeneity with high throughput and resolution, facilitating the profiling of single-cell transcriptomes. However, due to some experimental factors, a large number of missing values are generated in scRNA-seq data, which are called dropout events, and this phenomenon affects the downstream analysis. Imputation is an effective denoising method, but existing imputation methods still face a huge challenge: lack of interpretability. In this study, we propose single-cell Self-Attention Generative Adversarial Networks(scSAGAN), a semi-supervised imputation method for scRNA-seq data. scSAGAN mainly uses Semi-Supervised Learning (SSL) and Probabilistic Latent Semantic Analysis (PLSA), which can not only learn the potential characteristics of different types of cells but explain their imputation behavior. In clustering experiments, scSAGAN exhibits better clustering performance than all baselines on 7 datasets. Next, we interpret the imputation behavior of scSAGAN on datasets such as Alzheimer’s disease and find causative genes associated with the corresponding datasets. scSAGAN is currently an open-source method, available at https://github.com/zehaoxiongl23/scSAGAN.

MicroRNAs (miRNAs) are consistently capable of regulating gene expression synergistically in a combination mode and play a key role in various biological processes associated with the initiation and development of human diseases, which indicate that comprehending the synergistic molecular mechanism of miRNAs may facilitate understanding the pathogenesis of diseases or even overcome it. However, most existing computational methods had an incomprehensive acknowledge of the miRNA synergistic effect on the pathogenesis of complex diseases, or were hard to be extended to a large-scale prediction task of miRNA synergistic combinations for different diseases. In this article, we propose a novel tensor completion framework integrating multi-view miRNAs and diseases information, called miRCom, for the discovery of potential disease-associated miRNA-miRNA pairs. We first construct an incomplete three-order association tensor and several types of similarity matrices based on existing biological knowledge. Then, we formulate an objective function via performing the factorizations of coupled tensor and matrices simultaneously. Finally, we build an optimization schema by adopting the ADMM algorithm. After that, we obtain the prediction of miRNA-miRNA pairs for different diseases from the full tensor. The contrastive experimental results with other approaches verified that miRCom effectively identify the potential disease-related miRNA-miRNA pairs. Moreover, case study results further illustrated that miRNA-miRNA pairs have more biologically significance and prognostic value than single miRNAs.

MOTIVATION: Protein kinases have been the focus of drug discovery research for many years because they play a causal role in many human diseases. Understanding the binding profile of kinase inhibitors is a prerequisite for drug discovery, and traditional methods of predicting kinase inhibitors are time-consuming and inefficient. Calculation-based predictive methods provide a relatively low-cost and high-efficiency approach to the rapid development and effective understanding of the binding profile of kinase inhibitors. Particularly, the continuous improvement of network pharmacology methods provides unprecedented opportunities for drug discovery, network-based computational methods could be employed to aggregate the effective information from heterogeneous sources, which have become a new way for predicting the binding profile of kinase inhibitors. RESULTS: In this study, we proposed a network-based influence deep diffusion model, named IDDkin, for enhancing the prediction of kinase inhibitors. IDDkin uses deep graph convolutional networks, graph attention networks and adaptive weighting methods to diffuse the effective information of heterogeneous networks. The updated kinase and compound representations are used to predict potential compound-kinase pairs. The experimental results show that the performance of IDDkin is superior to the comparison methods, including the state-of-the-art kinase inhibitor prediction method and the classic model widely used in relationship prediction. In experiments conducted to verify its generalizability and in case studies, the IDDkin model also shows excellent performance. All of these results demonstrate the powerful predictive ability of the IDDkin model in the field of kinase inhibitors. AVAILABILITY AND IMPLEMENTATION: Source code and data can be downloaded from https://github.com/CS-BIO/IDDkin. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Many forms of variations exist in the human genome including single nucleotide polymorphism, small insert/deletion (DEL) (indel) and structural variation (SV). Somatically acquired SV may regulate the expression of tumor-related genes and result in cell proliferation and uncontrolled growth, eventually inducing tumor formation. Virus integration with host genome sequence is a type of SV that causes the related gene instability and normal cells to transform into tumor cells. Cancer SVs and viral integration sites must be discovered in a genome-wide scale for clarifying the mechanism of tumor occurrence and development. RESULTS: In this paper, we propose a new tool called seeksv to detect somatic SVs and viral integration events. Seeksv simultaneously uses split read signal, discordant paired-end read signal, read depth signal and the fragment with two ends unmapped. Seeksv can detect DEL, insertion, inversion and inter-chromosome transfer at single-nucleotide resolution. Different types of sequencing data, such as single-end sequencing data or paired-end sequencing data can accommodate to detect SV. Seeksv develops a rescue model for SV with breakpoints located in sequence homology regions. Results on simulated and real data from the 1000 Genomes Project and esophageal squamous cell carcinoma samples show that seeksv has higher efficiency and precision compared with other similar software in detecting SVs. For the discovery of hepatitis B virus integration sites from probe capture data, the verified experiments show that more than 90% viral integration sequences detected by seeksv are true. AVAILABILITY AND IMPLEMENTATION: seeksv is implemented in C ++ and can be downloaded from https://github.com/qkl871118/seeksv CONTACT: : [email protected] information: Supplementary data are available at Bioinformatics online.

The discovery of human disease-related miRNA is a challenging problem for complex disease biology research. For existing computational methods, it is difficult to achieve excellent performance with sparse known miRNA-disease association verified by biological experiment. Here, we develop CPTL, a Collective Prediction based on Transduction Learning, to systematically prioritize miRNAs related to disease. By combining disease similarity, miRNA similarity with known miRNA-disease association, we construct a miRNA-disease network for predicting miRNA-disease association. Then, CPTL calculates relevance score and updates the network structure iteratively, until a convergence criterion is reached. The relevance score of node including miRNA and disease is calculated by the use of transduction learning based on its neighbors. The network structure is updated using relevance score, which increases the weight of important links. To show the effectiveness of our method, we compared CPTL with existing methods based on HMDD datasets. Experimental results indicate that CPTL outperforms existing approaches in terms of AUC, precision, recall, and F1-score. Moreover, experiments performed with different number of iterations verify that CPTL has good convergence. Besides, it is analyzed that the varying of weighted parameters affect predicted results. Case study on breast cancer has further confirmed the identification ability of CPTL.