Clustering scRNA-seq data plays a vital role in scRNA-seq data analysis and downstream analyses. Many computational methods have been proposed and achieved remarkable results. However, there are several limitations of these methods. First, they do not fully exploit cellular features. Second, they are developed based on gene expression information and lack of flexibility in integrating intercellular relationships. Finally, the performance of these methods is affected by dropout event. We propose a novel deep learning (DL) model based on attention autoencoder and zero-inflated (ZI) layer, namely scAMZI, to cluster scRNA-seq data. scAMZI is mainly composed of SimAM (a Simple, parameter-free Attention Module), autoencoder, ZINB (Zero-Inflated Negative Binomial) model and ZI layer. Based on ZINB model, we introduce autoencoder and SimAM to reduce dimensionality of data and learn feature representations of cells and relationships between cells. Meanwhile, ZI layer is used to handle zero values in the data. We compare the performance of scAMZI with nine methods (three shallow learning algorithms and six state-of-the-art DL-based methods) on fourteen benchmark scRNA-seq datasets of various sizes (from hundreds to tens of thousands of cells) with known cell types. Experimental results demonstrate that scAMZI outperforms competing methods. scAMZI outperforms competing methods and can facilitate downstream analyses such as cell annotation, marker gene discovery, and cell trajectory inference. The package of scAMZI is made freely available at https://doi.org/10.5281/zenodo.13131559 .

Cell type annotation is a critical step in analyzing single-cell RNA sequencing (scRNA-seq) data. A large number of deep learning (DL)–based methods have been proposed to annotate cell types of scRNA-seq data and have achieved impressive results. However, there are several limitations to these methods. First, they do not fully exploit cell-to-cell differential features. Second, they are developed based on shallow features and lack of flexibility in integrating high-order features in the data. Finally, the low-dimensional gene features may lead to overfitting in neural networks. To overcome those limitations, we propose a novel DL-based model, cell type annotation of single-cell RNA-seq data using residual graph convolutional neural network with contrastive learning (scRGCL), based on residual graph convolutional neural network and contrastive learning for cell type annotation of single-cell RNA-seq data. scRGCL mainly consists of a residual graph convolutional neural network, contrastive learning, and weight freezing. A residual graph convolutional neural network is utilized to extract complex high-order features from data. Contrastive learning can help the model learn meaningful cell-to-cell differential features. Weight freezing can avoid overfitting and help the model discover the impact of specific gene expression on cell type annotation. To verify the effectiveness of scRGCL, we compared its performance with six methods (three shallow learning algorithms and three state-of-the-art DL-based methods) on eight single-cell benchmark datasets from two species (seven in human and one in mouse). Experimental results not only show that scRGCL outperforms competing methods but also demonstrate the generalizability of scRGCL for cell type annotation. scRGCL is available at https://github.com/nathanyl/scRGCL.

Introduction: Glioma is the most common malignant brain tumor in adults. Radiation is a key therapy in glioma. However, the radioresistance of glioma was a big challenge. HLA complex P5 (HCP5) has been reported dysregulated in several types of malignant tumor, including glioma. The role of HCP5 in the radiosensitivity of glioma is so far unknown. The present study aimed to investigate the effect of HCP5 on radiosensitivity in gliomas. Methods: The levels of HCP5 and microRNA (miR)-128 were detected using qRT-PCR. The cell growth curve was used to show the cell proliferation and evaluate the radiosensitivity of glioma cells following exposure to X-ray. Senescence-associated β-galactosidase (SA-β-Gal) staining was used to test the cellular senescence. Luciferase reporter and RNA immunoprecipitation (RIP) assays were performed to determine the correlation between HCP5 and miR-128. Results: HCP5 level of glioma cells was significantly higher than human astrocytes, whereas miR-128 level was lower in glioma cells. Besides, the HCP5 expression was increased in glioma tissues compared to normal brain tissues (NBTs). Knockdown of HCP5 inhibited cell proliferation and increased radiosensitivity in glioma cells. MiR-128 was predicted to be a target of HCP5. It was demonstrated that HCP5 directly bound to miR-128 and regulated its expression in glioma cells. Furthermore, the effects of HCP5 knockdown on radiosensitivity of glioma cells were attenuated by the inhibitor of miR-128. Conclusion: These findings suggested that interaction between lncRNA HCP5 and microRNA-128 could regulate the radiosensitivity of glioma cells by intervening in cellular senescence. This might be used as the potential radio-sensitization targets for glioma therapy.

Single-cell RNA sequencing (scRNA-seq) is currently an important technology for identifying cell types and studying diseases at the genetic level. Identifying rare cell types is biologically important as one of the downstream data analyses of single-cell RNA sequencing. Although rare cell identification methods have been developed, most of these suffer from insufficient mining of intercellular similarities, low scalability, and being time-consuming. In this paper, we propose a single-cell similarity division algorithm (scSID) for identifying rare cells. It takes cell-to-cell similarity into consideration by analyzing both inter-cluster and intra-cluster similarities, and discovers rare cell types based on the similarity differences. We show that scSID outperforms other existing methods by benchmarking it on different experimental datasets. Application of scSID to multiple datasets, including 68K PBMC and intestine, highlights its exceptional scalability and remarkable ability to identify rare cell populations.