CellEnBoost: A Boosting-Based Ligand-Receptor Interaction Identification Model for Cell-to-Cell Communication Inference

The diversification of methods for studying cell-cell interactions and communication

No cell lives in a vacuum, and the molecular interactions between cells define most phenotypes. Transcriptomics provides rich information to infer cell-cell interactions and communication, thus accelerating the discovery of the roles of cells within their communities. Such research relies heavily on algorithms that infer which cells are interacting and the ligands and receptors involved. Specific pressures on different research niches are driving the evolution of next-generation computational tools, enabling new conceptual opportunities and technological advances. More sophisticated algorithms now account for the heterogeneity and spatial organization of cells, multiple ligand types and intracellular signalling events, and enable the use of larger and more complex datasets, including single-cell and spatial transcriptomics. Similarly, new high-throughput experimental methods are increasing the number and resolution of interactions that can be analysed simultaneously. Here, we explore recent progress in cell-cell interaction research and highlight the diversification of the next generation of tools, which have yielded a rich ecosystem of tools for different applications and are enabling invaluable discoveries.

CVGAE: A Self-Supervised Generative Method for Gene Regulatory Network Inference Using Single-Cell RNA Sequencing Data

Gene regulatory network (GRN) inference based on single-cell RNA sequencing data (scRNAseq) plays a crucial role in understanding the regulatory mechanisms between genes. Various computational methods have been employed for GRN inference, but their performance in terms of network accuracy and model generalization is not satisfactory, and their poor performance is caused by high-dimensional data and network sparsity. In this paper, we propose a self-supervised method for gene regulatory network inference using single-cell RNA sequencing data (CVGAE). CVGAE uses graph neural network for inductive representation learning, which merges gene expression data and observed topology into a low-dimensional vector space. The well-trained vectors will be used to calculate mathematical distance of each gene, and further predict interactions between genes. In overall framework, FastICA is implemented to relief computational complexity caused by high dimensional data, and CVGAE adopts multi-stacked GraphSAGE layers as an encoder and an improved decoder to overcome network sparsity. CVGAE is evaluated on several single cell datasets containing four related ground-truth networks, and the result shows that CVGAE achieve better performance than comparative methods. To validate learning and generalization capabilities, CVGAE is applied in few-shot environment by change the ratio of train set and test set. In condition of few-shot, CVGAE obtains comparable or superior performance.

GEnDDn: An lncRNA-Disease Association Identification Framework Based on Dual-Net Neural Architecture and Deep Neural Network

Accumulating studies have demonstrated close relationships between long non-coding RNAs (lncRNAs) and diseases. Identification of new lncRNA-disease associations (LDAs) enables us to better understand disease mechanisms and further provides promising insights into cancer targeted therapy and anti-cancer drug design. Here, we present an LDA prediction framework called GEnDDn based on deep learning. GEnDDn mainly comprises two steps: First, features of both lncRNAs and diseases are extracted by combining similarity computation, non-negative matrix factorization, and graph attention auto-encoder, respectively. And each lncRNA-disease pair (LDP) is depicted as a vector based on concatenation operation on the extracted features. Subsequently, unknown LDPs are classified by aggregating dual-net neural architecture and deep neural network. Using six different evaluation metrics, we found that GEnDDn surpassed four competing LDA identification methods (SDLDA, LDNFSGB, IPCARF, LDASR) on the lncRNADisease and MNDR databases under fivefold cross-validation experiments on lncRNAs, diseases, LDPs, and independent lncRNAs and independent diseases, respectively. Ablation experiments further validated the powerful LDA prediction performance of GEnDDn. Furthermore, we utilized GEnDDn to find underlying lncRNAs for lung cancer and breast cancer. The results elucidated that there may be dense linkages between IFNG-AS1 and lung cancer as well as between HIF1A-AS1 and breast cancer. The results require further biomedical experimental verification. GEnDDn is publicly available at https://github.com/plhhnu/GEnDDn.

SEnSCA: Identifying possible ligand-receptor interactions and its application in cell-cell communication inference

Multicellular organisms have dense affinity with the coordination of cellular activities, which severely depend on communication across diverse cell types. Cell-cell communication (CCC) is often mediated via ligand-receptor interactions (LRIs). Existing CCC inference methods are limited to known LRIs. To address this problem, we developed a comprehensive CCC analysis tool SEnSCA by integrating single cell RNA sequencing and proteome data. SEnSCA mainly contains potential LRI acquisition and CCC strength evaluation. For acquiring potential LRIs, it first extracts LRI features and reduces the feature dimension, subsequently constructs negative LRI samples through K-means clustering, finally acquires potential LRIs based on Stacking ensemble comprising support vector machine, 1D-convolutional neural networks and multi-head attention mechanism. During CCC strength evaluation, SEnSCA conducts LRI filtering and then infers CCC by combining the three-point estimation approach and single cell RNA sequencing data. SEnSCA computed better precision, recall, accuracy, F1 score, AUC and AUPR under most of conditions when predicting possible LRIs. To better illustrate the inferred CCC network, SEnSCA provided three visualization options: heatmap, bubble diagram and network diagram. Its application on human melanoma tissue demonstrated its reliability in CCC detection. In summary, SEnSCA offers a useful CCC inference tool and is freely available at https://github.com/plhhnu/SEnSCA.

SCPLPA: An miRNA-disease association prediction model based on spatial consistency projection and label propagation algorithm

Identifying the association between miRNA and diseases is helpful for disease prevention, diagnosis and treatment. It is of great significance to use computational methods to predict potential human miRNA disease associations. Considering the shortcomings of existing computational methods, such as low prediction accuracy and weak generalization, we propose a new method called SCPLPA to predict miRNA-disease associations. First, a heterogeneous disease similarity network was constructed using the disease semantic similarity network and the disease Gaussian interaction spectrum kernel similarity network, while a heterogeneous miRNA similarity network was constructed using the miRNA functional similarity network and the miRNA Gaussian interaction spectrum kernel similarity network. Then, the estimated miRNA-disease association scores were evaluated by integrating the outcomes obtained by implementing label propagation algorithms in the heterogeneous disease similarity network and the heterogeneous miRNA similarity network. Finally, the spatial consistency projection algorithm of the network was used to extract miRNA disease association features to predict unverified associations between miRNA and diseases. SCPLPA was compared with four classical methods (MDHGI, NSEMDA, RFMDA and SNMFMDA), and the results of multiple evaluation metrics showed that SCPLPA exhibited the most outstanding predictive performance. Case studies have shown that SCPLPA can effectively identify miRNAs associated with colon neoplasms and kidney neoplasms. In summary, our proposed SCPLPA algorithm is easy to implement and can effectively predict miRNA disease associations, making it a reliable auxiliary tool for biomedical research.

Finding potential lncRNA-disease associations using a boosting-based ensemble learning model

Introduction: Long non-coding RNAs (lncRNAs) have been in the clinical use as potential prognostic biomarkers of various types of cancer. Identifying associations between lncRNAs and diseases helps capture the potential biomarkers and design efficient therapeutic options for diseases. Wet experiments for identifying these associations are costly and laborious. Methods: We developed LDA-SABC, a novel boosting-based framework for lncRNA-disease association (LDA) prediction. LDA-SABC extracts LDA features based on singular value decomposition (SVD) and classifies lncRNA-disease pairs (LDPs) by incorporating LightGBM and AdaBoost into the convolutional neural network. Results: The LDA-SABC performance was evaluated under five-fold cross validations (CVs) on lncRNAs, diseases, and LDPs. It obviously outperformed four other classical LDA inference methods (SDLDA, LDNFSGB, LDASR, and IPCAF) through precision, recall, accuracy, F1 score, AUC, and AUPR. Based on the accurate LDA prediction performance of LDA-SABC, we used it to find potential lncRNA biomarkers for lung cancer. The results elucidated that 7SK and HULC could have a relationship with non-small-cell lung cancer (NSCLC) and lung adenocarcinoma (LUAD), respectively. Conclusion: We hope that our proposed LDA-SABC method can help improve the LDA identification.

Identifying potential ligand-receptor interactions based on gradient boosted neural network and interpretable boosting machine for intercellular communication analysis

Cell-cell communication is essential to many key biological processes. Intercellular communication is generally mediated by ligand-receptor interactions (LRIs). Thus, building a comprehensive and high-quality LRI resource can significantly improve intercellular communication analysis. Meantime, due to lack of a "gold standard" dataset, it remains a challenge to evaluate LRI-mediated intercellular communication results. Here, we introduce CellGiQ, a high-confident LRI prediction framework for intercellular communication analysis. Highly confident LRIs are first inferred by LRI feature extraction with BioTriangle, LRI selection using LightGBM, and LRI classification based on ensemble of gradient boosted neural network and interpretable boosting machine. Subsequently, known and identified high-confident LRIs are filtered by combining single-cell RNA sequencing (scRNA-seq) data and further applied to intercellular communication inference through a quartile scoring strategy. To validation the predictions, CellGiQ exploited several evaluation strategies: using AUC and AUPR, it surpassed six competing LRI prediction models on four LRI datasets; through Venn diagrams and molecular docking, its predicted LRIs were validated by five other popular intercellular communication inference methods; based on the overlapping LRIs, it computed high Jaccard index with six other state-of-the-art intercellular communication prediction tools within human HNSCC tissues; by comparing with classical models and literature retrieve, its inferred HNSCC-related intercellular communication results was further validated. The novelty of this study is to identify high-confident LRIs based on machine learning as well as design several LRI validation ways, providing reference for computational LRI prediction. CellGiQ provides an open-source and useful tool to decompose LRI-mediated intercellular communication at single cell resolution. CellGiQ is freely available at https://github.com/plhhnu/CellGiQ.

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers

Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governing gene expression and disease-related mechanisms. Existing enhancer identification methods exhibit limitations, prompting the development of our novel multi-input deep learning framework, termed Enhancer-MDLF. Experimental results illustrate that Enhancer-MDLF outperforms the previous method, Enhancer-IF, across eight distinct human cell lines and exhibits superior performance on generic enhancer datasets and enhancer-promoter datasets, affirming the robustness of Enhancer-MDLF. Additionally, we introduce transfer learning to provide an effective and potential solution to address the prediction challenges posed by enhancer specificity. Furthermore, we utilize model interpretation to identify transcription factor binding site motifs that may be associated with enhancer regions, with important implications for facilitating the study of enhancer regulatory mechanisms. The source code is openly accessible at https://github.com/HaoWuLab-Bioinformatics/Enhancer-MDLF.

LDA-VGHB: identifying potential lncRNA-disease associations with singular value decomposition, variational graph auto-encoder and heterogeneous Newton boosting machine

Long noncoding RNAs (lncRNAs) participate in various biological processes and have close linkages with diseases. In vivo and in vitro experiments have validated many associations between lncRNAs and diseases. However, biological experiments are time-consuming and expensive. Here, we introduce LDA-VGHB, an lncRNA-disease association (LDA) identification framework, by incorporating feature extraction based on singular value decomposition and variational graph autoencoder and LDA classification based on heterogeneous Newton boosting machine. LDA-VGHB was compared with four classical LDA prediction methods (i.e. SDLDA, LDNFSGB, IPCARF and LDASR) and four popular boosting models (XGBoost, AdaBoost, CatBoost and LightGBM) under 5-fold cross-validations on lncRNAs, diseases, lncRNA-disease pairs and independent lncRNAs and independent diseases, respectively. It greatly outperformed the other methods with its prominent performance under four different cross-validations on the lncRNADisease and MNDR databases. We further investigated potential lncRNAs for lung cancer, breast cancer, colorectal cancer and kidney neoplasms and inferred the top 20 lncRNAs associated with them among all their unobserved lncRNAs. The results showed that most of the predicted top 20 lncRNAs have been verified by biomedical experiments provided by the Lnc2Cancer 3.0, lncRNADisease v2.0 and RNADisease databases as well as publications. We found that HAR1A, KCNQ1DN, ZFAT-AS1 and HAR1B could associate with lung cancer, breast cancer, colorectal cancer and kidney neoplasms, respectively. The results need further biological experimental validation. We foresee that LDA-VGHB was capable of identifying possible lncRNAs for complex diseases. LDA-VGHB is publicly available at https://github.com/plhhnu/LDA-VGHB.

Exploration of the molecular mechanism of intercellular communication in paediatric neuroblastoma by single-cell sequencing

Neuroblastoma (NB) is an embryonic tumour that originates in the sympathetic nervous system and occurs most often in infants and children under 2 years of age. Moreover, it is the most common extracranial solid tumour in children. Increasing studies suggest that intercellular communication within the tumour microenvironment is closely related to tumour development. This study aimed to construct a prognosis-related intercellular communication-associated genes model by single-cell sequencing and transcriptome sequencing to predict the prognosis of patients with NB for precise management. Single-cell data from patients with NB were downloaded from the gene expression omnibus database for comprehensive analysis. Furthermore, prognosis-related genes were screened in the TARGET database based on epithelial cell marker genes through a combination of Cox regression and Lasso regression analyses, using GSE62564 and GSE85047 for external validation. The patients' risk scores were calculated, followed by immune infiltration analysis, drug sensitivity analysis, and enrichment analysis of risk scores, which were conducted for the prognostic model. I used the Lasso regression feature selection algorithm to screen characteristic genes in NB and developed a 21-gene prognostic model. The risk scores were highly correlated with multiple immune cells and common anti-tumour drugs. Furthermore, the risk score was identified as an independent prognostic factor for NB. In this study, I constructed and validated a prognostic signature based on epithelial marker genes, which may provide useful information on the development and prognosis of NB.

DlncRNALoc: A discrete wavelet transform-based model for predicting lncRNA subcellular localization

The prediction of long non-coding RNA (lncRNA) subcellular localization is essential to the understanding of its function and involvement in cellular regulation. Traditional biological experimental methods are costly and time-consuming, making computational methods the preferred approach for predicting lncRNA subcellular localization (LSL). However, existing computational methods have limitations due to the structural characteristics of lncRNAs and the uneven distribution of data across subcellular compartments. We propose a discrete wavelet transform (DWT)-based model for predicting LSL, called DlncRNALoc. We construct a physicochemical property matrix of a 2-tuple bases based on lncRNA sequences, and we introduce a DWT lncRNA feature extraction method. We use the Synthetic Minority Over-sampling Technique (SMOTE) for oversampling and the local fisher discriminant analysis (LFDA) algorithm to optimize feature information. The optimized feature vectors are fed into support vector machine (SVM) to construct a predictive model. DlncRNALoc has been applied for a five-fold cross-validation on the three sets of benchmark datasets. Extensive experiments have demonstrated the superiority and effectiveness of the DlncRNALoc model in predicting LSL.

STGNNks: Identifying cell types in spatial transcriptomics data based on graph neural network, denoising auto-encoder, and k-sums clustering

BACKGROUND

Spatial transcriptomics technologies fully utilize spatial location information, tissue morphological features, and transcriptional profiles. Integrating these data can greatly advance our understanding about cell biology in the morphological background.

METHODS

We developed an innovative spatial clustering method called STGNNks by combining graph neural network, denoising auto-encoder, and k-sums clustering. First, spatial resolved transcriptomics data are preprocessed and a hybrid adjacency matrix is constructed. Next, gene expressions and spatial context are integrated to learn spots' embedding features by a deep graph infomax-based graph convolutional network. Third, the learned features are mapped to a low-dimensional space through a zero-inflated negative binomial (ZINB)-based denoising auto-encoder. Fourth, a k-sums clustering algorithm is developed to identify spatial domains by combining k-means clustering and the ratio-cut clustering algorithms. Finally, it implements spatial trajectory inference, spatially variable gene identification, and differentially expressed gene detection based on the pseudo-space-time method on six 10x Genomics Visium datasets.

RESULTS

We compared our proposed STGNNks method with five other spatial clustering methods, CCST, Seurat, stLearn, Scanpy and SEDR. For the first time, four internal indicators in the area of machine learning, that is, silhouette coefficient, the Davies-Bouldin index, the Caliniski-Harabasz index, and the S_Dbw index, were used to measure the clustering performance of STGNNks with CCST, Seurat, stLearn, Scanpy and SEDR on five spatial transcriptomics datasets without labels (i.e., Adult Mouse Brain (FFPE), Adult Mouse Kidney (FFPE), Human Breast Cancer (Block A Section 2), Human Breast Cancer (FFPE), and Human Lymph Node). And two external indicators including adjusted Rand index (ARI) and normalized mutual information (NMI) were applied to evaluate the performance of the above six methods on Human Breast Cancer (Block A Section 1) with real labels. The comparison experiments elucidated that STGNNks obtained the smallest Davies-Bouldin and S_Dbw values and the largest Silhouette Coefficient, Caliniski-Harabasz, ARI and NMI, significantly outperforming the above five spatial transcriptomics analysis algorithms. Furthermore, we detected the top six spatially variable genes and the top five differentially expressed genes in each cluster on the above five unlabeled datasets. And the pseudo-space-time tree plot with hierarchical layout demonstrated a flow of Human Breast Cancer (Block A Section 1) progress in three clades branching from three invasive ductal carcinoma regions to multiple ductal carcinoma in situ sub-clusters.

CONCLUSION

We anticipate that STGNNks can efficiently improve spatial transcriptomics data analysis and further boost the diagnosis and therapy of related diseases. The codes are publicly available at https://github.com/plhhnu/STGNNks.

Predicting potential microbe-disease associations with graph attention autoencoder, positive-unlabeled learning, and deep neural network

Background

Microbes have dense linkages with human diseases. Balanced microorganisms protect human body against physiological disorders while unbalanced ones may cause diseases. Thus, identification of potential associations between microbes and diseases can contribute to the diagnosis and therapy of various complex diseases. Biological experiments for microbe-disease association (MDA) prediction are expensive, time-consuming, and labor-intensive.

Methods

We developed a computational MDA prediction method called GPUDMDA by combining graph attention autoencoder, positive-unlabeled learning, and deep neural network. First, GPUDMDA computes disease similarity and microbe similarity matrices by integrating their functional similarity and Gaussian association profile kernel similarity, respectively. Next, it learns the feature representation of each microbe-disease pair using graph attention autoencoder based on the obtained disease similarity and microbe similarity matrices. Third, it selects a few reliable negative MDAs based on positive-unlabeled learning. Finally, it takes the learned MDA features and the selected negative MDAs as inputs and designed a deep neural network to predict potential MDAs.

Results

GPUDMDA was compared with four state-of-the-art MDA identification models (i.e., MNNMDA, GATMDA, LRLSHMDA, and NTSHMDA) on the HMDAD and Disbiome databases under five-fold cross validations on microbes, diseases, and microbe-disease pairs. Under the three five-fold cross validations, GPUDMDA computed the best AUCs of 0.7121, 0.9454, and 0.9501 on the HMDAD database and 0.8372, 0.8908, and 0.8948 on the Disbiome database, respectively, outperforming the other four MDA prediction methods. Asthma is the most common chronic respiratory condition and affects ~339 million people worldwide. Inflammatory bowel disease is a class of globally chronic intestinal disease widely existed in the gut and gastrointestinal tract and extraintestinal organs of patients. Particularly, inflammatory bowel disease severely affects the growth and development of children. We used the proposed GPUDMDA method and found that Enterobacter hormaechei had potential associations with both asthma and inflammatory bowel disease and need further biological experimental validation.

Conclusion

The proposed GPUDMDA demonstrated the powerful MDA prediction ability. We anticipate that GPUDMDA helps screen the therapeutic clues for microbe-related diseases.

Predicting potential lncRNA biomarkers for lung cancer and neuroblastoma based on an ensemble of a deep neural network and LightGBM

Introduction: Lung cancer is one of the most frequent neoplasms worldwide with approximately 2.2 million new cases and 1.8 million deaths each year. The expression levels of programmed death ligand-1 (PDL1) demonstrate a complex association with lung cancer. Neuroblastoma is a high-risk malignant tumor and is mainly involved in childhood patients. Identification of new biomarkers for these two diseases can significantly promote their diagnosis and therapy. However, in vivo experiments to discover potential biomarkers are costly and laborious. Consequently, artificial intelligence technologies, especially machine learning methods, provide a powerful avenue to find new biomarkers for various diseases. Methods: We developed a machine learning-based method named LDAenDL to detect potential long noncoding RNA (lncRNA) biomarkers for lung cancer and neuroblastoma using an ensemble of a deep neural network and LightGBM. LDAenDL first computes the Gaussian kernel similarity and functional similarity of lncRNAs and the Gaussian kernel similarity and semantic similarity of diseases to obtain their similar networks. Next, LDAenDL combines a graph convolutional network, graph attention network, and convolutional neural network to learn the biological features of the lncRNAs and diseases based on their similarity networks. Third, these features are concatenated and fed to an ensemble model composed of a deep neural network and LightGBM to find new lncRNA-disease associations (LDAs). Finally, the proposed LDAenDL method is applied to identify possible lncRNA biomarkers associated with lung cancer and neuroblastoma. Results: The experimental results show that LDAenDL computed the best AUCs of 0.8701, 107 0.8953, and 0.9110 under cross-validation on lncRNAs, diseases, and lncRNA-disease pairs on Dataset 1, respectively, and 0.9490, 0.9157, and 0.9708 on Dataset 2, respectively. Furthermore, AUPRs of 0.8903, 0.9061, and 0.9166 under three cross-validations were obtained on Dataset 1, and 0.9582, 0.9122, and 0.9743 on Dataset 2. The results demonstrate that LDAenDL significantly outperformed the other four classical LDA prediction methods (i.e., SDLDA, LDNFSGB, IPCAF, and LDASR). Case studies demonstrate that CCDC26 and IFNG-AS1 may be new biomarkers of lung cancer, SNHG3 may associate with PDL1 for lung cancer, and HOTAIR and BDNF-AS may be potential biomarkers of neuroblastoma. Conclusion: We hope that the proposed LDAenDL method can help the development of targeted therapies for these two diseases.