The development of single-cell RNA sequencing (scRNA-seq) technology provides unprecedented opportunities for elucidating cell heterogeneity and gene expression. Identifying and discovering cell types through cell clustering is a crucial step in analyzing scRNA-seq data. However, the high-dimensionality nature and frequent dropout events of the data raise great challenges for cell clustering. Here, we propose a novel contrastive clustering framework called scSCCNIA (Similarity-matrix-based Contrastive Clustering with Neighbor Information Aggregation), for the accurate identification of cell clusters from scRNA-seq data. scSCCNIA adopts a Laplacian filter to conduct neighbor information aggregation, constructs different graph views by using special un-shared parameters Siamese encoders for data augmentation, and learns the latent low-dimensional embedding representations via similarity-matrix-based contrastive learning. Comparative analyses of multiple scRNA-seq datasets from different platforms and with varying cell numbers demonstrate that scSCCNIA outperforms existing methods in terms of cell clustering and marker gene identification. Furthermore, scSCCNIA reveals the heterogeneity and functional specificity of various cell types through Gene Ontology terms and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. Overall, scSCCNIA is an effective algorithm for learning latent features from scRNA-seq data, enhancing cell type identification accuracy and facilitating downstream analyses of scRNA-seq data.

Single-cell multi-omics sequencing represents an advanced technology capable of simultaneously measuring multiple omics data from the same cell. The joint clustering of single-cell multi-omics sequencing data enables a comprehensive depiction of cell states and uncovers intricate molecular mechanisms, holding immense significance in fields such as oncology, neurology, and developmental biology. However, the disparities in feature spaces across different omics layers and data noise present substantial challenges for achieving accurate clustering. To tackle these challenges, we introduce a novel clustering method for single-cell multi-omics data, termed scMSAC, which is grounded in a denoising subgraph attention autoencoder. The proposed method employs a weighted nearest neighbor graph strategy to ascertain the weights of multi-omics data, subsequently generating a similarity graph that holistically encapsulates intercellular connections through the weighted amalgamation of diverse omics perspectives. The scMSAC model captures the topological features of cells through the subgraph attention autoencoder, constructing relationships among cells. For the omics features extracted by the subgraph attention autoencoder, scMSAC incorporates an SCA (Spatial Channel Attention) mechanism for feature fusion to reduce the differences in feature spaces of different omics and achieve better clustering performance. Comparative experiments with various existing methods demonstrate that scMSAC has excellent clustering performance and performs well in detecting rare cell types and differential expression analysis.

Single-cell RNA sequencing (scRNA-seq) provides transcriptome profiling of individual cells, allowing for in-depth studies of cell heterogeneity at cell resolution. While cell clustering lays the basic foundation of scRNA-seq data analysis, the high-dimensionality and frequent dropout events of the data raise great challenges. Although plenty of dedicated clustering methods have been proposed, they often fail to fully explore the underlying data structure. Here, we introduce scMCGF, a new multi-view clustering algorithm based on graph fusion. It utilizes multi-view data generated from transcriptomic data to learn the consistent and complementary information across different view, ultimately constructing a unified graph matrix for robust cell clustering. Specifically, scMCGF utilizes two-dimensional-reduction methods (principal component analysis and diffusion maps) to capture both linear and non-linear characteristics of the data. Additionally, it calculates a cell-pathway score matrix to incorporate pathway-level information. These three features, along with the pre-processed gene expression data, form the multi-view data. scMCGF iteratively refines the structure of similarity graphs of each view through adaptive learning and learns a unified graph matrix by weighting and fusing the individual similarity graph matrix. The final clustering results are obtained by applying the rank constraint on the Laplacian matrix of the unified graph matrix. Experiments results of 13 real data sets reveal that scMCGF outperforms eight state-of-the-art methods in clustering accuracy and robustness. Furthermore, biological analysis validates that the clustering results of scMCGF provide a reliable foundation for downstream investigations.

Cluster assignment is vital to analyzing single-cell RNA sequencing (scRNA-seq) data to understand high-level biological processes. Deep learning-based clustering methods have recently been widely used in scRNA-seq data analysis. However, existing deep models often overlook the interconnections and interactions among network layers, leading to the loss of structural information within the network layers. Herein, we develop a new self-supervised clustering method based on an adaptive multi-scale autoencoder, called scAMAC. The self-supervised clustering network utilizes the Multi-Scale Attention mechanism to fuse the feature information from the encoder, hidden and decoder layers of the multi-scale autoencoder, which enables the exploration of cellular correlations within the same scale and captures deep features across different scales. The self-supervised clustering network calculates the membership matrix using the fused latent features and optimizes the clustering network based on the membership matrix. scAMAC employs an adaptive feedback mechanism to supervise the parameter updates of the multi-scale autoencoder, obtaining a more effective representation of cell features. scAMAC not only enables cell clustering but also performs data reconstruction through the decoding layer. Through extensive experiments, we demonstrate that scAMAC is superior to several advanced clustering and imputation methods in both data clustering and reconstruction. In addition, scAMAC is beneficial for downstream analysis, such as cell trajectory inference. Our scAMAC model codes are freely available at https://github.com/yancy2024/scAMAC.

Atmospheric phase screen (APS) is the dominant error source of InSAR. The spatial-temporal variations of APS in interferograms can lead to incorrect interpretation of phase and inaccurate extraction of surface deformation. In recent years, deep learning denoising models have been applied to study the features of APS in InSAR interferograms and extract deformation information in existing researches. However, the characteristics of APS are diverse, and it is difficult to extract this knowledge using a deep learning model based on limited interferograms that contain APS. Moreover, existing methods mainly use synthetic data to train the model and remove APS. In order to provide sufficient APS data for training deep learning networks, this paper proposes a new method for generating atmospheric phase samples. This method is based on the auxiliary classifier generative adversarial network (ACGAN) to generate more APS samples from available interferograms, fully learning the atmospheric delay errors related to terrain, atmospheric turbulence, and heavy rainfall, and generating atmospheric phase screen interferogram samples with multiple features. The technique is applied to 235 interferograms collected over Hangzhou on ascending orbit number 108 between January 12, 2020, and December 3, 2022, to generate different types of atmospheric sample screen interferogram samples. The network has shown great potential for the removal of atmospheric phase in InSAR research.

A large number of works have presented the single-cell RNA sequencing (scRNA-seq) to study the diversity and biological functions of cells at the single-cell level. Clustering identifies unknown cell types, which is essential for downstream analysis of scRNA-seq samples. However, the high dimensionality, high noise and pervasive dropout rate of scRNA-seq samples have a significant challenge to the cluster analysis of scRNA-seq samples. Herein, we propose a new adaptive fuzzy clustering model based on the denoising autoencoder and self-attention mechanism called the scDASFK. It implements the comparative learning to integrate cell similar information into the clustering method and uses a deep denoising network module to denoise the data. scDASFK consists of a self-attention mechanism for further denoising where an adaptive clustering optimization function for iterative clustering is implemented. In order to make the denoised latent features better reflect the cell structure, we introduce a new adaptive feedback mechanism to supervise the denoising process through the clustering results. Experiments on 16 real scRNA-seq datasets show that scDASFK performs well in terms of clustering accuracy, scalability and stability. Overall, scDASFK is an effective clustering model with great potential for scRNA-seq samples analysis. Our scDASFK model codes are freely available at https://github.com/LRX2022/scDASFK.

The advances in single-cell ribonucleic acid sequencing (scRNA-seq) allow researchers to explore cellular heterogeneity and human diseases at cell resolution. Cell clustering is a prerequisite in scRNA-seq analysis since it can recognize cell identities. However, the high dimensionality, noises and significant sparsity of scRNA-seq data have made it a big challenge. Although many methods have emerged, they still fail to fully explore the intrinsic properties of cells and the relationship among cells, which seriously affects the downstream clustering performance. Here, we propose a new deep contrastive clustering algorithm called scDCCA. It integrates a denoising auto-encoder and a dual contrastive learning module into a deep clustering framework to extract valuable features and realize cell clustering. Specifically, to better characterize and learn data representations robustly, scDCCA utilizes a denoising Zero-Inflated Negative Binomial model-based auto-encoder to extract low-dimensional features. Meanwhile, scDCCA incorporates a dual contrastive learning module to capture the pairwise proximity of cells. By increasing the similarities between positive pairs and the differences between negative ones, the contrasts at both the instance and the cluster level help the model learn more discriminative features and achieve better cell segregation. Furthermore, scDCCA joins feature learning with clustering, which realizes representation learning and cell clustering in an end-to-end manner. Experimental results of 14 real datasets validate that scDCCA outperforms eight state-of-the-art methods in terms of accuracy, generalizability, scalability and efficiency. Cell visualization and biological analysis demonstrate that scDCCA significantly improves clustering and facilitates downstream analysis for scRNA-seq data. The code is available at https://github.com/WJ319/scDCCA.

Rapid development of single-cell RNA sequencing (scRNA-seq) technology has allowed researchers to explore biological phenomena at the cellular scale. Clustering is a crucial and helpful step for researchers to study the heterogeneity of cell. Although many clustering methods have been proposed, massive dropout events and the curse of dimensionality in scRNA-seq data make it still difficult to analysis because they reduce the accuracy of clustering methods, leading to misidentification of cell types. In this work, we propose the scHFC, which is a hybrid fuzzy clustering method optimized by natural computation based on Fuzzy C Mean (FCM) and Gath-Geva (GG) algorithms. Specifically, principal component analysis algorithm is utilized to reduce the dimensions of scRNA-seq data after it is preprocessed. Then, FCM algorithm optimized by simulated annealing algorithm and genetic algorithm is applied to cluster the data to output a membership matrix, which represents the initial clustering result and is taken as the input for GG algorithm to get the final clustering results. We also develop a cluster number estimation method called multi-index comprehensive estimation, which can estimate the cluster numbers well by combining four clustering effectiveness indexes. The performance of the scHFC method is evaluated on 17 scRNA-seq datasets, and compared with six state-of-the-art methods. Experimental results validate the better performance of our scHFC method in terms of clustering accuracy and stability of algorithm. In short, scHFC is an effective method to cluster cells for scRNA-seq data, and it presents great potential for downstream analysis of scRNA-seq data. The source code is available at https://github.com/WJ319/scHFC.

Synthetic aperture radar (SAR) interferometer (InSAR) is now a key geodetic tool for monitoring the surface displacement. Thanks to ESA's Sentinel-1 sensors with IW mode as its default acquisition mode for land observations and its free access data policy, which have global coverage at moderate resolution with about 20m, national scale InSAR-based deformation is being studied in recent years by using big data techniques such as high performance computing and cloud computing. In this paper, we proposed the time series InSAR technique called Coherent-Scatterers InSAR (CS-InSAR) and its parallel solution for processing the whole CS-InSAR chain of Sentinel-1 data automatically and efficiently, considering the characteristics of CS-InSAR algorithm, such as frequent I/O data flow and heavy computation. By developing the parallelized CS-InSAR algorithm on the Big Earth Data Platform, 11922 satellite SAR data from September 2018 to December 2019 over China were processed, and the preliminary national InSAR-based surface deformation mapping for 2018–2019 was produced, with the deformation accuracy better than 0.6 cm in urban area.

Tanggula Mountain is located in the hinterland of Qinghai-Tibet Plateau (QTP), and the spatial distribution of permafrost has relatively strong heterogeneity. In recent years, permafrost is quickly degrading due to climate warming and human activities. The freeze-thaw cycles of the active layer on the permafrost cause seasonal uplift and subsidence. And it is difficult to accurately retrieve the surface deformation using the Temporarily Coherent Point synthetic aperture radar interferometry (TCPInSAR) in low-coherent permafrost areas, because there are few coherent targets identified in this area. To improve the density of measurement points, an improved TCPInSAR technique, namely Distributed Scatterers and Coherent Targets InSAR (DS-CTInSAR) is proposed in this paper. The Anderson-Darling (AD) test is used to extract statistically homogeneous pixels (SHP), and the regularized M-estimators method is adopted to estimate the covariance matrix, then the eigenvalue decomposition (EVD) method is used to estimate the optimal phase in this process. Applying this DS-CTInSAR algorithm to 29-C band Sentinel-1 images with a 12 days revisit time from 2019/1/10 to 2019/12/24, we find that this technology greatly improves the density of measurement points, and compared with the NSBAS technology, the two results are consistent, exhibiting good correlations 0.91. The InSAR results show that the average annual deformation rate is −24.87~23.61mm/yr in the study area.

The bank of Nu River is controlled by several faults, and the geological environment is fragile. It is also affected by the natural factors of atmospheric precipitation. So there are a large number of potential landslide hidden points along Nu River. In this research we proposed an improved Time series InSAR (TSInSAR) technology for landslide investigation and identification from Anmuda to Kaxi along Nu River, China. The technique can identify high coherent scatterers and accurately obtain the surface deformation information. From the deformation rate map and Google earth optical image we identify five potential landslide hidden points along Nu River. We discover the continued acceleration phenomenon of hidden points that had been identified so that timely warnings of landslides with major potential safety hazards. This work is also helpful for the local government to carry out geological survey and disaster monitoring.

Jinsha River Valley area is located in the complex fault zone of southwest China. As the region has been strongly affected by landslide disaster in the past few years, it is very imperative to detect the regions with large deformation around this area. InSAR technique is widely used in obtaining the terrain displacement, especially for this inaccessible areas. Thus this work utilizes time series InSAR (TSInSAR) method to investigate the landslide risk for the Ruba-Lagang section of Jinsha River by using Sentinel-1 images. Several landslide-hidden areas are identified and the deformation time series for them are analyzed, which will be of great importance to the disaster prevention and local infrastructure construction.