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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Programmed cell death (PCD) is an essential process for development, and shows conserved cytological features in both plants and animals. Caspases are well‐known critical components of the PCD machinery in animals. However, currently few typical counterparts have been identified in plants and only several caspase‐like proteases are known to be involved in plant PCD, indicating the existence of great challenge for confirming new caspase‐like proteases and elucidating the mechanisms regulating plant PCD. Here, we report a novel cysteine protease, NtTPE8, which was extracted from tobacco seeds and confirmed as a new caspase‐like protease. Recombinant NtTPE8 exhibited legumain and caspase‐like proteolytic activities, both of which could be inhibited by the pan‐caspase inhibitor (Z‐VAD‐FMK). Notably, NtTPE8 possessed several caspase activities and the capacity to cleave the cathepsin H substrate FVR, indicating a unique character of NtTPE8. NtTPE8 was exclusively expressed in the integumentary tapetum and thus, is the first specific molecular marker reported to date for this cell type. Down‐regulation of NtTPE8 caused seed abortion, via disturbing early embryogenesis, indicating its critical role in embryogenesis and seed development. In conclusion, we identified a novel caspase‐like cysteine protease, NtTPE8, exclusively expressed in the integumentary tapetum that is involved in seed development. [ABSTRACT FROM AUTHOR]