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Spatial transcriptome technology can parse transcriptomic data at the spatial level to detect high-throughput gene expression and preserve information regarding the spatial structure of tissues. Identifying spatial domains, that is identifying regions with similarities in gene expression and histology, is the most basic and critical aspect of spatial transcriptome data analysis. Most current methods identify spatial domains only through a single view, which may obscure certain important information and thus fail to make full use of the information embedded in spatial transcriptome data. Therefore, we propose an unsupervised clustering framework based on multiview graph convolutional networks (MVST) to achieve accurate spatial domain recognition by the learning graph embedding features of neighborhood graphs constructed from gene expression information, spatial location information, and histopathological image information through multiview graph convolutional networks. By exploring spatial transcriptomes from multiple views, MVST enables data from all parts of the spatial transcriptome to be comprehensively and fully utilized to obtain more accurate spatial expression patterns. We verified the effectiveness of MVST on real spatial transcriptome datasets, the robustness of MVST on some simulated datasets, and the reasonableness of the framework structure of MVST in ablation experiments, and from the experimental results, it is clear that MVST can achieve a more accurate spatial domain identification compared with the current more advanced methods. In conclusion, MVST is a powerful tool for spatial transcriptome research with improved spatial domain recognition. Author summary: Spatial transcriptome sequencing can not only reveal the mechanisms of disease development, but can also be used to explore the structure of biological tissues, which are widely used in a variety of fields such as developmental biology and oncology. To utilize spatial transcriptome data to understand how different cells work together to perform complex functions, spatially relevant groups of cells must be identified, which leads to the task of dissecting tissues into spatial domains. However, most of the currently available spatial domain identification tools have yet to fully utilize the information embedded in spatial transcriptomic data, and their identification remains to be improved for the fine-grained exploration of tissue structure. Here, we developed a tool for accurate spatial domain identification, and our tool makes full use of the various types of spatial transcriptome data from multiple perspectives, identifying the real grouping information in spatial transcriptome data as much as possible. Our results show that our spatial domain identification tool can identify spatial domains more accurately and provide effective help for biological organization structure exploration. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data. Author summary: It is still difficult to analyze scRNA-seq data because a significant portion of expressed genes have zeros. Gene expression levels can be restored through the imputation of scRNA-seq data, facilitating downstream analysis. To overcome this challenge, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. Through the use of several simulated and real datasets, we perform an comprehensively evaluation of scRNMF against existing methods. scRNMF can enhance various aspects of downstream analysis, including gene expression data recovery, cell clustering analysis, gene differential expression analysis, and cellular trajectory reconstruction. The results of our study demonstrate that scRNMF is a powerful tool that can improve the accuracy of single-cell data analysis. [ABSTRACT FROM AUTHOR]

Background: Enhancers play a crucial role in gene regulation, and some active enhancers produce noncoding RNAs known as enhancer RNAs (eRNAs) bi-directionally. The most commonly used method for detecting eRNAs is CAGE-seq, but the instability of eRNAs in vivo leads to data noise in sequencing results. Unfortunately, there is currently a lack of research focused on the noise inherent in CAGE-seq data, and few approaches have been developed for predicting eRNAs. Bridging this gap and developing widely applicable eRNA prediction models is of utmost importance. Results: In this study, we proposed a method to reduce false positives in the identification of eRNAs by adjusting the statistical distribution of expression levels. We also developed eRNA prediction models using joint gene expressions, DNA methylation, and histone modification. These models achieved impressive performance with an AUC value of approximately 0.95 for intra-cell prediction and 0.9 for cross-cell prediction. Conclusions: Our method effectively attenuates the noise generated by stochastic RNA production, resulting in more accurate detection of eRNAs. Furthermore, our eRNA prediction model exhibited significant accuracy in both intra-cell and cross-cell validation, highlighting its robustness and potential application in various cellular contexts. [ABSTRACT FROM AUTHOR]

The gene regulatory structure of cells involves not only the regulatory relationship between two genes, but also the cooperative associations of multiple genes. However, most gene regulatory network inference methods for single cell only focus on and infer the regulatory relationships of pairs of genes, ignoring the global regulatory structure which is crucial to identify the regulations in the complex biological systems. Here, we proposed a graph-based Deep learning model for Regulatory networks Inference among Genes (DeepRIG) from single-cell RNA-seq data. To learn the global regulatory structure, DeepRIG builds a prior regulatory graph by transforming the gene expression of data into the co-expression mode. Then it utilizes a graph autoencoder model to embed the global regulatory information contained in the graph into gene latent embeddings and to reconstruct the gene regulatory network. Extensive benchmarking results demonstrate that DeepRIG can accurately reconstruct the gene regulatory networks and outperform existing methods on multiple simulated networks and real-cell regulatory networks. Additionally, we applied DeepRIG to the samples of human peripheral blood mononuclear cells and triple-negative breast cancer, and presented that DeepRIG can provide accurate cell-type-specific gene regulatory networks inference and identify novel regulators of progression and inhibition. Author summary: Although many methods have been proposed to infer the gene regulatory network of a single cell, they only focus on the regulatory relationships of pairs of genes and ignore the global regulatory structure. Here, we present a deep learning-based model to learn the global regulatory structure and reconstruct the gene regulatory networks from single-cell RNA sequencing data with a graph view. We utilize the weighted gene co-expression analysis to build a prior regulatory graph of gene and a graph autoencoder to deconstruct the latent regulatory structure among genes. We performed extensive experiments on varieties of single-cell RNA sequencing datasets and compared our method with 9 stat-of-the-art gene regulatory network inference method. The results show that our method can significantly improve the accuracy of gene regulatory network inference and can be applied to identify key regulators in a wide range of scenarios. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) is a revolutionary breakthrough that determines the precise gene expressions on individual cells and deciphers cell heterogeneity and subpopulations. However, scRNA-seq data are much noisier than traditional high-throughput RNA-seq data because of technical limitations, leading to many scRNA-seq data studies about dimensionality reduction and visualization remaining at the basic data-stacking stage. In this study, we propose an improved variational autoencoder model (termed DREAM) for dimensionality reduction and a visual analysis of scRNA-seq data. Here, DREAM combines the variational autoencoder and Gaussian mixture model for cell type identification, meanwhile explicitly solving 'dropout' events by introducing the zero-inflated layer to obtain the low-dimensional representation that describes the changes in the original scRNA-seq dataset. Benchmarking comparisons across nine scRNA-seq datasets show that DREAM outperforms four state-of-the-art methods on average. Moreover, we prove that DREAM can accurately capture the expression dynamics of human preimplantation embryonic development. DREAM is implemented in Python, freely available via the GitHub website, https://github.com/Crystal-JJ/DREAM. [ABSTRACT FROM AUTHOR]

Motivation Single-cell RNA sequencing (scRNA-seq) technology attracts extensive attention in the biomedical field. It can be used to measure gene expression and analyze the transcriptome at the single-cell level, enabling the identification of cell types based on unsupervised clustering. Data imputation and dimension reduction are conducted before clustering because scRNA-seq has a high 'dropout' rate, noise and linear inseparability. However, independence of dimension reduction, imputation and clustering cannot fully characterize the pattern of the scRNA-seq data, resulting in poor clustering performance. Herein, we propose a novel and accurate algorithm, SSNMDI, that utilizes a joint learning approach to simultaneously perform imputation, dimensionality reduction and cell clustering in a non-negative matrix factorization (NMF) framework. In addition, we integrate the cell annotation as prior information, then transform the joint learning into a semi-supervised NMF model. Through experiments on 14 datasets, we demonstrate that SSNMDI has a faster convergence speed, better dimensionality reduction performance and a more accurate cell clustering performance than previous methods, providing an accurate and robust strategy for analyzing scRNA-seq data. Biological analysis are also conducted to validate the biological significance of our method, including pseudotime analysis, gene ontology and survival analysis. We believe that we are among the first to introduce imputation, partial label information, dimension reduction and clustering to the single-cell field. Availability and implementation The source code for SSNMDI is available at https://github.com/yushanqiu/SSNMDI. [ABSTRACT FROM AUTHOR]

We propose the hierarchical Projective Adaptive Resonance Theory (PART) algorithm for classification of gene expression data. This algorithm is realized by combing transposed quasi-supervised PART and unsupervised PART. We develop the corresponding validation statistics for each process and compare it with other clustering algorithms in a case study of tuberculosis (TB). First, we use sample-based transposed quasi-supervised PART to obtain optimal clustering results of samples distinguished by time post-infection and the representative genes for each cluster including up-regulated, down-regulated and stable genes. The up- and down-regulated genes show more than 90% similarity to the result derived from Linear Models for Microarray Data and are verified by weighted k-nearest neighbor model on TB projection. Second, we use gene-based unsupervised PART algorithm to cluster these representative genes where functional enrichment analysis is conducted in each cluster. We further confirm the main immune response of human macrophage-like THP-1 cells against TB within 2 days is type I interferon-mediated innate immunity. This study demonstrates how hierarchical PART algorithm analyzes microarray data. The sample-based quasi-supervised PART extracts representative genes and narrows down the shortlist of disease-relevant genes and gene-based unsupervised PART classifies representative genes that help to interpret immune response against TB. [ABSTRACT FROM AUTHOR]

Metastasis is a major cause of cancer morbidity and mortality, and most cancer deaths are caused by cancer metastasis rather than by the primary tumor. The prediction of metastasis based on computational methods has not been explored much in the previous research. In this study, we proposed a graph convolutional network embedded with a graph learning (GL) module, named glmGCN, to predict the distant metastasis of cancer. Both the mRNA and lncRNA expressions were used to provide more genetic information than using the mRNA alone and we used them to construct gene interaction graph representation to consider the effect of genetic interaction. Then, the prediction of the cancer metastasis was performed under a GCN framework, which extracted informative and advanced features from the built non-regular graph structures. Particularly, a GL module was embedded in the proposed glmGCN to learn an optimal graph representation of the gene interaction. We firstly constructed the protein–protein interaction network to represent the initial gene(node) relationship graph. Then, through the GL module, a new graph representation was built which optimally learned the gene interaction strength. Finally, the GCN was adopted to identify the distant metastasis cases. It is worth mentioning that the proposed method pays more attentions on the gene–gene relation than the previous GCN-based method, so more accurate prediction performance can be obtained. The glmGCN was trained based on two types of cancer and was further validated using two other cancer types. A series of experiments have shown that the effectiveness of the proposed method. The implementation for the proposed method is available at https://github.com/RanSuLab/Metastasis-glmGCN. [ABSTRACT FROM AUTHOR]

Lots of biological processes are controlled by gene regulatory networks (GRNs), such as growth and differentiation of cells, occurrence and development of the diseases. Therefore, it is important to persistently concentrate on the research of GRN. The determination of the gene–gene relationships from gene expression data is a complex issue. Since it is difficult to efficiently obtain the regularity behind the gene-gene relationship by only relying on biochemical experimental methods, thus various computational methods have been used to construct GRNs, and some achievements have been made. In this paper, we propose a novel method MMFGRN (for "Multi-source Multi-model Fusion for Gene Regulatory Network reconstruction") to reconstruct the GRN. In order to make full use of the limited datasets and explore the potential regulatory relationships contained in different data types, we construct the MMFGRN model from three perspectives: single time series data model, single steady-data model and time series and steady-data joint model. And, we utilize the weighted fusion strategy to get the final global regulatory link ranking. Finally, MMFGRN model yields the best performance on the DREAM4 InSilico_Size10 data, outperforming other popular inference algorithms, with an overall area under receiver operating characteristic score of 0.909 and area under precision-recall (AUPR) curves score of 0.770 on the 10-gene network. Additionally, as the network scale increases, our method also has certain advantages with an overall AUPR score of 0.335 on the DREAM4 InSilico_Size100 data. These results demonstrate the good robustness of MMFGRN on different scales of networks. At the same time, the integration strategy proposed in this paper provides a new idea for the reconstruction of the biological network model without prior knowledge, which can help researchers to decipher the elusive mechanism of life. [ABSTRACT FROM AUTHOR]

Single-cell RNA sequencing (scRNA-seq) has enabled researchers to study gene expression at the cellular level. However, due to the extremely low levels of transcripts in a single cell and technical losses during reverse transcription, gene expression at a single-cell resolution is usually noisy and highly dimensional; thus, statistical analyses of single-cell data are a challenge. Although many scRNA-seq data analysis tools are currently available, a gold standard pipeline is not available for all datasets. Therefore, a general understanding of bioinformatics and associated computational issues would facilitate the selection of appropriate tools for a given set of data. In this review, we provide an overview of the goals and most popular computational analysis tools for the quality control, normalization, imputation, feature selection and dimension reduction of scRNA-seq data. [ABSTRACT FROM AUTHOR]

Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fortunately, deep learning technology can contribute toward resolving these challenges. There is evidence that deep learning can handle omics data well and resolve omics problems. This survey aims to provide an entry-level guideline for researchers, to understand and use deep learning in order to solve omics problems. We first introduce several deep learning models and then discuss several research areas which have combined omics and deep learning in recent years. In addition, we summarize the general steps involved in using deep learning which have not yet been systematically discussed in the existent literature on this topic. Finally, we compare the features and performance of current mainstream open source deep learning frameworks and present the opportunities and challenges involved in deep learning. This survey will be a good starting point and guideline for omics researchers to understand deep learning. [ABSTRACT FROM AUTHOR]

Motivation: A clear identification of the primary site of tumor is of great importance to the next targeted site-specific treatments and could efficiently improve patient's overall survival. Even though many classifiers based on gene expression had been proposed to predict the tumor primary, only a few studies focus on using DNA methylation (DNAm) profiles to develop classifiers, and none of them compares the performance of classifiers based on different profiles. Results: We introduced novel selection strategies to identify highly tissue-specific CpG sites and then used the random forest approach to construct the classifiers to predict the origin of tumors. We also compared the prediction performance by applying similar strategy on miRNA expression profiles. Our analysis indicated that these classifiers had an accuracy of 96.05% (Maximum-Relevance- Maximum-Distance: 90.02-99.99%) or 95.31% (principal component analysis: 79.82-99.91%) on independent DNAm datasets, and an overall accuracy of 91.30% (range 79.33-98.74%) on independent miRNA test sets for predicting tumor origin. This suggests that our feature selection methods are very effective to identify tissue-specific biomarkers and the classifiers we developed can efficiently predict the origin of tumors. We also developed a user-friendly webserver that helps users to predict the tumor origin by uploadingmiRNA expression or DNAmprofile of their interests. Availability and implementation: The webserver, and relative data, code are accessible at http:// server.malab.cn/MMCOP/. [ABSTRACT FROM AUTHOR]

Although microRNAs (miRNAs) have been widely studied as epigenetic regulation molecules, fewer studies focus on the gender difference at the miRNA and isomiR expression levels. In this study, we aim to understand the potential relationships between gender difference and miRNA/isomiR expression through a comprehensive analysis of small RNA-sequencing datasets based on different human diseases and tissues. Based on specific samples from males and females, we determined that some miRNAs may be diversely expressed between different tissues and genders. Thus, these miRNAs may exhibit inconsistent and even opposite expression between males and females. According to deregulated miRNA expression profiles, some dominantly expressed miRNA loci were selected to analyze isomiR expression patterns using rates of dominant isomiRs. In some miRNA loci, isomiRs showed statistical significance between tumor and normal samples and between males and females samples, suggesting that isomiR expression patterns are not always invariable but may vary between males and females, as well as among different tissues, tumors, and normal samples. The divergence implicates the fluctuation in the expression of miRNA and its detailed expression at the isomiR levels. The divergence also indicates that gender difference may be an important factor that affects the screening of disease-associated miRNAs and isomiRs. This study suggests that miRNA/isomiR expression and gender difference may be more complex than previously assumed and should be further studied according to specific samples from males or females. [ABSTRACT FROM AUTHOR]

Pattern genes are a group of genes that have a modularized expression behavior under serial physiological conditions. The identification of pattern genes will provide a path toward a global and dynamic understanding of gene functions and their roles in particular biological processes or events, such as development and pathogenesis. In this study, we present PaGenBase, a novel repository for the collection of tissue- and time-specific pattern genes, including specific genes, selective genes, housekeeping genes and repressed genes. The PaGenBase database is now freely accessible at http://bioinf.xmu.edu.cn/PaGenBase/. In the current version (PaGenBase 1.0), the database contains 906,599 pattern genes derived from the literature or from data mining of more than 1,145,277 gene expression profiles in 1,062 distinct samples collected from 11 model organisms. Four statistical parameters were used to quantitatively evaluate the pattern genes. Moreover, three methods (quick search, advanced search and browse) were designed for rapid and customized data retrieval. The potential applications of PaGenBase are also briefly described. In summary, PaGenBase will serve as a resource for the global and dynamic understanding of gene function and will facilitate high-level investigations in a variety of fields, including the study of development, pathogenesis and novel drug discovery. [ABSTRACT FROM AUTHOR]

Abstract: Little is known about the potential role of microRNAs (miRNAs) in the carcinogenesis of gastric cancer induced by Helicobacter pylori (H. pylori). Here, we showed that microRNA-222 (miR-222) was up-regulated in H. pylori-infected gastric mucosa and gastric cancer. Ectopic expression of miR-222 promoted cell proliferation and colony formation in vitro. Mechanistically, we identified RECK as a novel target of miR-222, and also confirmed their relationship by the inverse correlation of mRNA expression ex vivo. Furthermore, we found that RNA interference silencing of RECK can mimic the oncogenic effects of miR-222. Collectively, H. pylori may function as an initiator in the process of carcinogenesis by up-regulating miR-222, which further participates in the progression of cancer by promoting proliferation and inhibiting RECK. [Copyright &y& Elsevier]

Transcriptomic studies of mental disorders using the human brain tissues have been limited, and gene expression signatures in schizophrenia (SCZ) remain elusive. In this study, we applied three differential co-expression methods to analyze five transcriptomic datasets (three RNA-Seq and two microarray datasets) derived from SCZ and matched normal postmortem brain samples. We aimed to uncover biological pathways where internal correlation structure was rewired or inter-coordination was disrupted in SCZ. In total, we identified 60 rewired pathways, many of which were related to neurotransmitter, synapse, immune, and cell adhesion. We found the hub genes, which were on the center of rewired pathways, were highly mutually consistent among the five datasets. The combinatory list of 92 hub genes was generally multi-functional, suggesting their complex and dynamic roles in SCZ pathophysiology. In our constructed pathway crosstalk network, we found "Clostridium neurotoxicity" and "signaling events mediated by focal adhesion kinase" had the highest interactions. We further identified disconnected gene links underlying the disrupted pathway crosstalk. Among them, four gene pairs (PAK1:SYT1, PAK1:RFC5, DCTN1:STX1A, and GRIA1:MAP2K4) were normally correlated in universal contexts. In summary, we systematically identified rewired pathways, disrupted pathway crosstalk circuits, and critical genes and gene links in schizophrenia transcriptomes. [ABSTRACT FROM AUTHOR]

Xyloglucan endotransglucosylase/hydrolases (XTHs)—a family of xyloglucan modifying enzymes—play an essential role in the construction and restructuring of xyloglucan cross-links. However, no comprehensive study has been performed on this gene family in barley. A total of 24 HvXTH genes (named HvXTH1-24) and an EG16 member were identified using the recently completed genomic database of barley (Hordeum vulgare). Phylogenetic analysis showed that 24 HvXTH genes could be classified into three phylogenetic groups: (I/II, III-A and III-B) and HvXTH15 was in the ancestral group. All HvXTH protein members—except HvXTH15—had a conserved N-glycosylation site. The genomic location of HvXTHs on barley chromosomes showed that the 24 genes are unevenly distributed on the 7 chromosomes, with 10 of them specifically located on chromosome 7H. A structure-based sequence alignment demonstrates that each XTH possesses a highly conserved domain (ExDxE) responsible for catalytic activity. Expression profiles based on the barley genome database showed that HvXTH family members display different expression patterns in different tissues and at different stages. This study is the first systematic genomic analysis of the barley HvXTH gene family. Our results provide valuable information that will help to elucidate the roles of HvXTH genes in the growth and development of barley. [ABSTRACT FROM AUTHOR]

Discovering the common modules that are co-expressed across various stages can lead to an improved understanding of the underlying molecular mechanisms of cancers. There is a shortage of efficient tools for integrative analysis of gene expression and protein interaction networks for discovering common modules associated with cancer progression. To address this issue, we propose a novel regularized multi-view subspace clustering (rMV-spc) algorithm to obtain a representation matrix for each stage and a joint representation matrix that balances the agreement across various stages. To avoid the heterogeneity of data, the protein interaction network is incorporated into the objective of rMV-spc via regularization. Based on the interior point algorithm, we solve the optimization problem to obtain the common modules. By using artificial networks, we demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy. Furthermore, the rMV-spc discovers common modules in breast cancer networks based on the breast data, and these modules serve as biomarkers to predict stages of breast cancer. The proposed model and algorithm effectively integrate heterogeneous data for dynamic modules. [ABSTRACT FROM AUTHOR]

Summary: Pattern Gene Finder (PaGeFinder) is a web-based server for on-line detection of gene expression patterns from serial transcriptomic data generated by high-throughput technologies like microarray or next-generation sequencing. Three particular parameters, the specificity measure, the dispersion measure and the contribution measure, were introduced and implemented in PaGeFinder to help quantitative and interactive identification of pattern genes like housekeeping genes, specific (selective) genes and repressed genes. Besides the on-line computation service, the PaGeFinder also provides downloadable Java programs for local detection of gene expression patterns.Availability: http://bioinf.xmu.edu.cn:8080/PaGeFinder/index.jspContact: appo@xmu.edu.cn; zouquan@xmu.edu.cn [ABSTRACT FROM PUBLISHER]

Abstract: Escherichia coli secreted protein A (EspA) is a component of the type 3 secretion system (T3SS). The high level of expression when self-stimulated suggests that EspA may be used as a fusion partner. In the present study, EspA was used as a “fusion partner” to construct a fusion expression system, pEspA, in order to improve the expression and solubility of proteins from prokaryotes and eukaryotes. Target proteins were linked to the C-terminus of EspA by a linker containing a YAPQDP sequence, multiple cloning sites and an enterkinase cleavage site. Six proteins, IL-24, Stx2A1, Stx2B, S1, IntiminC300 and GFP, were expressed as EspA-fusion proteins using this vector. The expression level of each protein was enhanced by EspA and the majority of them (Stx2B, IntiminC300, GFP, Stx2A1, IL-24) were expressed in soluble form. EspA-fusion proteins can be purified by affinity chromatography (Sepharose chelated with EspA-specific monoclonal antibody) and by Ni2+ affinity chromatography for they contain a 6× His tag at their C-terminus. In addition, IL-24 remains soluble and demonstrates certain anti-tumor activity after the removal of EspA by enterkinase. The EspA fusion expression system was efficient in enhancing expression levels and the solubility of target proteins. [Copyright &y& Elsevier]