Single-cell three-dimensional (3D) genomics plays a vital role in understanding the spatial organization of chromatin within the cell nucleus. It provides insights into how chromatin structures influence regulatory mechanisms and cellular functions that are specific to different cell types and states. Analyzing differential interactions-the fundamental units of the 3D genome-at the single-cell level is crucial for extracting this valuable information. However, single-cell 3D genome data such as single-cell High-throughput Chromatin Conformation Capture (scHi-C) data are inherently sparse, noisy and heterogeneous, posing significant challenges that limit the downstream analyses. Although existing computational methods offer helpful solutions for identifying differential interactions at the single-cell level, most of them do not explicitly account for intrinsic statistical properties of the data, nor do they comprehensively incorporate both within-sample and between-sample covariates. In this study, we developed a statistical method named DISHIC (Differential Interaction analysis in single-cell Hi-C) to perform differential interaction analysis in scHi-C data. DISHIC leverages the Zero-Inflated Negative Binomial-based Wavelet (ZINB-WaVE) model, making it well-suited for high-dimensional zero-inflated count data with high dispersion. It models each bin pair in each sample independently while incorporating both bin-pair-level and cell-level covariates, allowing it to effectively capture noise and heterogeneity in sparse scHi-C data. This approach can detect differential interactions with greater accuracy and reliability while also allowing users to flexibly define covariates as input based on their specific needs. We evaluated the performance of DISHIC using both real and simulated datasets, demonstrating its enhanced effectiveness compared to existing state-of-the-art methods under various conditions. Furthermore, we conducted a comprehensive case study analyzing multi-omics datasets from two types of glial cells. This study explored the intricate relationships among chromatin interactions, gene expressions, and epigenetic modifications, providing new insights into cell-type-specific regulatory mechanisms.

The three-dimensional (3D) genome refers to exploring the spatial arrangement of chromatins within the cell nucleus. Analyzing differential interactions, the basic units of the 3D genome, particularly at the single-cell level, is crucial for revealing cell-type-specific functions and states. However, the sparsity and heterogeneity of single-cell 3D genome data pose significant challenges. Although existing methods offer helpful solutions, most of them lack the design for data distribution and cell-specific characteristics. Here, we developed a new method, DISHIC, for identifying differential interactions in single-cell high-throughput Chromatin Conformation Capture (scHi-C) data. Based on the ZINB-WaVE model, DISHIC independently models each bin pair, accounting for both bin-pair-level and cell-level covariates. We validated DISHIC's effectiveness and demonstrated its enhanced effectiveness compared to other methods. The code is available at https://github.com/zhaoyichao777/DISHIC.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, supplies a differentiable characteristic to address the problem for the identification of low, slow and small unmanned aerial vehicles (LSS UAVs). However, the primary challenge for the estimation of m-D parameters is how to separate weak rotation signal from Doppler signal and other interferences. Theoretically, null space pursuit (NSP) is an operator-based signal decomposition approach to decompose a signal into additive subcomponents. The premise of NSP is that two separated components are orthogonal. However, due to the different modulation models, rotation signal, Doppler signal or other interferences could not satisfy the condition. Moreover, traditional multi-order differential operator is not suitable for the decomposition of m-D signal. In thi1s paper, back projection strategy with instantaneous orthogonal NSP (BPIO-NSP) is proposed to distill Doppler signal, and then micro-Doppler NSP (MD-NSP) is jointly developed to separate rotation signal for the identification of LSS UAVs. Firstly, the decomposed component after NSP is applied to the short-time Fourier transform (STFT) to find the segments with instantaneous non-orthogonal property. Secondly, the back projection strategy is developed in BPIO-NSP to acquire the instantaneous orthogonal data, so as to adjust the decomposed Doppler signal with high accuracy. Finally, customized operator is specially constructed in MD-NSP to achieve the required rotation signal from the residue. Simulation results verify the theoretical analysis, and measured data of the fun and UAV detection experiments suggest that the proposed methods could be served for the application of the identification of LSS UAVs.

MOTIVATION: Identifying disease-related genes is an important issue in computational biology. Module structure widely exists in biomolecule networks, and complex diseases are usually thought to be caused by perturbations of local neighborhoods in the networks, which can provide useful insights for the study of disease-related genes. However, the mining and effective utilization of the module structure is still challenging in such issues as a disease gene prediction. RESULTS: We propose a hybrid disease-gene prediction method integrating multiscale module structure (HyMM), which can utilize multiscale information from local to global structure to more effectively predict disease-related genes. HyMM extracts module partitions from local to global scales by multiscale modularity optimization with exponential sampling, and estimates the disease relatedness of genes in partitions by the abundance of disease-related genes within modules. Then, a probabilistic model for integration of gene rankings is designed in order to integrate multiple predictions derived from multiscale module partitions and network propagation, and a parameter estimation strategy based on functional information is proposed to further enhance HyMM's predictive power. By a series of experiments, we reveal the importance of module partitions at different scales, and verify the stable and good performance of HyMM compared with eight other state-of-the-arts and its further performance improvement derived from the parameter estimation. CONCLUSIONS: The results confirm that HyMM is an effective framework for integrating multiscale module structure to enhance the ability to predict disease-related genes, which may provide useful insights for the study of the multiscale module structure and its application in such issues as a disease-gene prediction.

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, introduces significant characteristics to identify small unmanned aerial vehicles (UAVs) in remote surveillance. As opposed to the Doppler signal induced by the translation, m-D signal is comparatively weak and consists of multiple frequency components. In this letter, we propose synchrosqueezing phase analysis (SPA) for the extraction of rotation signal with the multichannel radar. Based on the proposed signal model, this new method not only enables multivariate denoising and sharpening time-frequency (TF) representation, but also concentrates on the energy of rotation signal for the separation. Simulations are employed to demonstrate the validity of the proposed method in extracting the m-D features. Applications on field data further prove the potential in delineating m-D characteristics with higher precision and render that this technique is promising for the identification of small UAVs.

Micro-Doppler (m-D) effect, induced by the rotation of rotor blades, provides an important signature to discriminate between small unmanned aerial vehicles (UAVs) and other aircrafts or birds in remote surveillance. Compared with the Doppler signal induced by the translation, m-D signal, however, is rather weak and consists of multiple frequency components. In this paper, empirical mode decomposition (EMD) algorithm is applied to addressing the mode-mixing problem in the returned signal. Theoretically, Doppler features are consequently allocated in the first few intrinsic mode functions (IMFs). Rather, the partial components of the subsequent IMFs hold a similar property with the rotation signal. Those components are selected as the input data for the sparse recovery. With the sinusoidal frequency-modulated basis (SFMB), the essence of the recovery problem is converted into 1-D parameter optimization. Then, phase orthogonal matching pursuit (POMP) method is developed for the sparse solution. The proposed method is contrasted with the prevailing approach to solving the mode-mixing problem. Simulation results confirm the theoretical analysis, showing the feasibility in the estimation of m-D frequency. The preliminary findings from the measured data suggest that the proposed method has a potential application in the identification of small UAVs.