Single-cell multi-omics techniques, which enable the simultaneous measurement of multiple modalities such as RNA gene expression and Assay for Transposase-Accessible Chromatin (ATAC) within individual cells, have become a powerful tool for deciphering the intricate complexity of cellular systems. Most current methods rely on motif databases to establish cross-modality relationships between genes from RNA-seq data and peaks from ATAC-seq data. However, these approaches are constrained by incomplete database coverage, particularly for novel or poorly characterized relationships. To address these limitations, we introduce single-cell Multi-omics Integration (scMI), a heterogeneous graph embedding method that encodes both cells and modality features from single-cell RNA-seq and ATAC-seq data into a shared latent space by learning cross-modality relationships. By modeling cells and modality features as distinct node types, we design an inter-type attention mechanism to effectively capture long-range cross-modality interactions between genes and peaks. Benchmark results demonstrate that embeddings learned by scMI preserve more biological information and achieve comparable or superior performance in downstream tasks including modality prediction, cell clustering, and gene regulatory network inference compared to methods that rely on databases. Furthermore, scMI significantly improves the alignment and integration of unmatched multi-omics data, enabling more accurate embedding and improved outcomes in downstream tasks.

As modern networks face increasing demands for superior service and management, Encrypted Traffic Classification (ETC) technology has become increasingly crucial. Considering that traffic data is easy to collect but hard to label, self-supervised ETC methods have attracted more and more attention. Compared to popular methods based on traffic images and text, traffic tables are simple to construct and more suitable for the flow-packet structure. However, existing methods have two problems: (1) The lack of data augmentation methods for tables weakens the performance of self-supervised learning. (2) Most methods only focus on single feature and cannot make full use of distinct features of traffic tables, such as temporal feature. To solve these problems, we propose a multi-feature fusion method based self-supervised learning approach for traffic tables. A new data augmentation method called Random Subsets Selection (RSS) is introduced alongside an effective fusion approach. In this way, temporal features can be successfully extracted and concatenated with the latent representations of input traffic tables. Experimental results from two open datasets and one self-collected dataset have shown that on imbalanced datasets, our method can effectively solve ETC problems even with a small number of labeled data. Empirically, both classification performance and processing speed are improved. Specifically, compared to the state-of-the-art tabular self-supervised learning method, our method achieves the better classification results on all datasets while the processing speed increases by almost two times, from 1.83 tables per second to 3.76 tables per second.

Cell type classification serves as one of the most fundamental analyses in bioinformatics. It helps recognizing various cells in cancer microenvironment, discovering new cell types and facilitating other downstream tasks. Single-cell RNA-sequencing (scRNA-seq) technology can profile the whole transcriptome of each cell, thus enabling cell type classification. However, high-dimensional scRNA-seq data pose serious challenges on cell type classification. Existing methods either classify the cells with reliance on the prior knowledge or by using neural networks whose massive parameters are hard to interpret. In this paper, we propose two novel attention-based models for cell type classification on single-cell RNA-seq data. The first model, Cell Feature Attention Network (CFAN), captures the features of a cell and performs attention model on them. To further improve interpretation, the second model, Cell-Gene Representation Attention Network (CGRAN), directly concretizes tokens as cells and genes and uses the cell representation renewed by self-attention over the cell and the genes to predict cell type. Both models show excellent performance in cell type classification; additionally, the key genes with high attention weights in CGRAN indicate and identify the marker genes of the cell types, thus proving the model’s biological interpretation.

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) enables the high-throughput profiling of gene expression at the single-cell level. However, overwhelming dropouts within data may obscure meaningful biological signals. Various imputation methods have recently been developed to address this problem. Therefore, it is important to perform a systematic evaluation of different imputation algorithms. RESULTS: In this study, we evaluated 11 of the most recent imputation methods on 12 real biological datasets from immunological studies and 4 simulated datasets. The performance of these methods was compared, based on numerical recovery, cell clustering and marker gene analysis. Most of the methods brought some benefits on numerical recovery. To some extent, the performance of imputation methods varied among protocols. In the cell clustering analysis, no method performed consistently well across all datasets. Some methods performed poorly on real datasets but excellent on simulated datasets. Surprisingly and importantly, some methods had a negative effect on cell clustering. In marker gene analysis, some methods identified potentially novel cell subsets. However, not all of the marker genes were successfully imputed in gene expression, suggesting that imputation challenges remain. CONCLUSIONS: In summary, different imputation methods showed different effects on different datasets, suggesting that imputation may have dataset specificity. Our study reveals the benefits and limitations of various imputation methods and provides a data-driven guidance for scRNA-seq data analysis.

A network can effectively depict close relationships among its nodes, with labels in a taxonomy describing the nodes' rich attributes. Network embedding aims at learning a representation vector for each node and label to preserve their proximity, while most existing methods suffer from serious underfitting when dealing with datasets with dense node-label links. For instance, a node could have dozens of labels describing its diverse properties, causing the single node vector overloaded and hard to fit all the labels. We propose HIerarchical Multi-vector Embedding (HIME), which solves the underfitting problem by adaptively learning multiple 'branch vectors' for each node to dynamically fit separate sets of labels in a hierarchy-aware embedding space. Moreover, a 'root vector' is learned for each node based on its branch vectors to better predict the sparse but valuable node-node links with the knowledge of its labels. Experiments reveal HIME’s comprehensive advantages over existing methods on tasks such as proximity search, link prediction and hierarchical classification.

MOTIVATION: Emerging single-cell RNA sequencing (scRNA-seq) technology empowers biological research at cellular level. One of the most crucial scRNA-seq data analyses is clustering single cells into subpopulations. However, the high variability, high sparsity and high dimensionality of scRNA-seq data pose lots of challenges for clustering analysis. Although many single-cell clustering methods have been recently developed, few of them fully exploit latent relationship among cells, thus leading to suboptimal clustering results. RESULTS: Here, we propose a novel unsupervised clustering method, scGAC (single-cell Graph Attentional Clustering), for scRNA-seq data. scGAC firstly constructs a cell graph and refines it by network denoising. Then, it learns clustering-friendly representation of cells through a graph attentional autoencoder, which propagates information across cells with different weights and captures latent relationship among cells. Finally, scGAC adopts a self-optimizing method to obtain the cell clusters. Experiments on 16 real scRNA-seq datasets show that scGAC achieves excellent performance and outperforms existing state-of-art single-cell clustering methods. AVAILABILITY AND IMPLEMENTATION: Python implementation of scGAC is available at Github (https://github.com/Joye9285/scGAC) and Figshare (https://figshare.com/articles/software/scGAC/19091348). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Gene regulatory networks (GRNs) consist of gene regulations between transcription factors (TFs) and their target genes. Single-cell RNA sequencing (scRNA-seq) brings both opportunities and challenges to the inference of GRNs. On the one hand, scRNA-seq data reveals statistic information of gene expressions at the single-cell resolution, which is conducive to the construction of GRNs; on the other hand, noises and dropouts pose great difficulties on the analysis of scRNA-seq data, causing low prediction accuracy by traditional methods. In this paper, we propose 3D Co-Expression Matrix Analysis (3DCEMA), which predicts regulatory relationships by classifying 3D co-expression matrices of gene triples using a 3D convolutional neural network. We found that by introducing a third gene as a comparison factor, our method can avoid the disturbance of noises and dropouts, and significantly increase the prediction accuracy of regulations between gene pairs. Compared with other existing GRN inference algorithms on both in-silico datasets and scRNA-Seq datasets, our algorithm based on deep learning shows higher stability and accuracy in the task of GRN inference.

We present the EKNOT system that automatically discovers major events from online news articles, connects each event to its discussion in Twitter, and provides a comprehensive summary of the events from both news media and social media's point of view. EKNOT takes a time period as input and outputs a complete picture of the events within the given time range along with the public opinions. For each event, EKNOT provides multi-dimensional summaries: a) a summary from news for an objective description; b) a summary from tweets containing opinions/sentiments; c) an entity graph which illustrates the major players involved and their correlations; d) the time span of the event; and e) an opinion (sentiment) distribution. Also, if a user is interested in a particular event, he/she can zoom into this event to investigate its aspects (sub-events) summarized in the same manner. EKNOT is built on real-time crawled news articles and tweets, allowing users to explore the dynamics of major events with minimal delays.

A major event often has repercussions on both news media and microblogging sites such as Twitter. Reports from mainstream news agencies and discussions from Twitter complement each other to form a complete picture. An event can have multiple aspects (sub-events) describing it from multiple angles, each of which attracts opinions/comments posted on Twitter. Mining such reflections is interesting to both policy makers and ordinary people seeking information. In this paper, we propose a unified framework to mine multi-aspect reflections of news events in Twitter. We propose a novel and efficient dynamic hierarchical entity-aware event discovery model to learn news events and their multiple aspects. The aspects of an event are linked to their reflections in Twitter by a bootstrapped dataless classification scheme, which elegantly handles the challenges of selecting informative tweets under overwhelming noise and bridging the vocabularies of news and tweets. In addition, we demonstrate that our framework naturally generates an informative presentation of each event with entity graphs, time spans, news summaries and tweet highlights to facilitate user digestion.

Recent years have witnessed the wide proliferation of geo-sensory applications wherein a bundle of sensors are deployed at different locations to cooperatively monitor the target condition. Given massive geo-sensory data, we study the problem of mining spatial co-evolving patterns (SCPs), i.e., groups of sensors that are spatially correlated and co-evolve frequently in their readings. SCP mining is of great importance to various real-world applications, yet it is challenging because (1) the truly interesting evolutions are often flooded by numerous trivial fluctuations in the geo-sensory time series; and (2) the pattern search space is extremely large due to the spatiotemporal combinatorial nature of SCP. In this paper, we propose a two-stage method called Assember. In the first stage, Assember filters trivial fluctuations using wavelet transform and detects frequent evolutions for individual sensors via a segment-and-group approach. In the second stage, Assember generates SCPs by assembling the frequent evolutions of individual sensors. Leveraging the spatial constraint, it conceptually organizes all the SCPs into a novel structure called the SCP search tree, which facilitates the effective pruning of the search space to generate SCPs efficiently. Our experiments on both real and synthetic data sets show that Assember is effective, efficient, and scalable.

Clean drinking water and safe water supply is vital to our life. Recent advances in technologies have made it possible to deploy smart sensor networks in large water distribution networks to monitor and identify the water quality online. In such a large-scale real-time monitoring application, large amounts of data stream out of multiple concurrent sensors continuously. In this paper, we present a system to monitor and analyze the sensor data streams online, find and summarize the spatio-temporal distribution patterns and correlations in co-evolving data, detect contamination events rapidly and facilitate corrective actions or notification. The system consists of an online data mining engine and a GUI providing the user with the current patterns discovered in the network, and an alerter notifying the user if there is anomalous water quality in the network.