Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Existing imputation methods rely on strict data assumptions, limiting their broader application, and lack reliable supervision, leading to biased signal recovery. To address these challenges, authors developed Bis, a distribution-agnostic deep learning model for accurately recovering missing sing-cell gene expression from multiple platforms. Bis is an optimal transport-based autoencoder model that can capture the intricate distribution of scRNA-seq data while addressing the characteristic sparsity by regularizing the cellular embedding space. Additionally, they propose a module using bulk RNA-seq data to guide reconstruction and ensure expression consistency. Experimental results show Bis outperforms other models across simulated and real datasets, showcasing superiority in various downstream analyses including batch effect removal, clustering, differential expression analysis, and trajectory inference. Moreover, Bis successfully restores gene expression levels in rare cell subsets in a tumor-matched peripheral blood dataset, revealing developmental characteristics of cytokine-induced natural killer cells within a head and neck squamous cell carcinoma microenvironment.

Bioinformatics-driven discovery of silica nanoparticles induces apoptosis and renal damage via the unfolded protein response in NRK-52E cells and rat kidney.

Silica nanoparticles (SiNPs) are nanomaterials with widespread applications in drug delivery and disease diagnosis. Despite their utility, SiNPs can cause chronic kidney disease, hindering their clinical translation. The molecular mechanisms underlying SiNP-induced renal toxicity are complex and require further investigation. To address this challenge, we employed bioinformatics tools to predict the potential mechanisms underlying renal damage caused by SiNPs. We identified 1627 upregulated differentially expressed genes (DEGs) and 1334 downregulated DEGs. Functional enrichment analysis and protein-protein interaction network revealed that SiNP-induced renal damage is associated with apoptosis. Subsequently, we verified that SiNPs induced apoptosis in an in vitro model of NRK-52E cells via the unfolded protein response (UPR) in a dose-dependent manner. Furthermore, in an in vivo rat model, high-dose SiNP administration via tracheal drip caused hyalinization of the renal tubules, renal interstitial lymphocytic infiltration, and collagen fiber accumulation. Concurrently, we observed an increase in UPR-related protein levels at the onset of renal damage. Thus, our study confirmed that SiNPs induce apoptosis and renal damage through the UPR, adding to the theoretical understanding of SiNP-related kidney damage and offering a potential target for preventing and treating kidney injuries in SiNP clinical applications.

Topological identification and interpretation for single-cell gene regulation elucidation across multiple platforms using scMGCA.

Single-cell RNA sequencing provides high-throughput gene expression information to explore cellular heterogeneity at the individual cell level. A major challenge in characterizing high-throughput gene expression data arises from challenges related to dimensionality, and the prevalence of dropout events. To address these concerns, we develop a deep graph learning method, scMGCA, for single-cell data analysis. scMGCA is based on a graph-embedding autoencoder that simultaneously learns cell-cell topology representation and cluster assignments. We show that scMGCA is accurate and effective for cell segregation and batch effect correction, outperforming other state-of-the-art models across multiple platforms. In addition, we perform genomic interpretation on the key compressed transcriptomic space of the graph-embedding autoencoder to demonstrate the underlying gene regulation mechanism. We demonstrate that in a pancreatic ductal adenocarcinoma dataset, scMGCA successfully provides annotations on the specific cell types and reveals differential gene expression levels across multiple tumor-associated and cell signalling pathways.

Hyperspectral Image Denoising via Weighted Multidirectional Low-Rank Tensor Recovery.

Recently, low-rank tensor recovery methods based on subspace representation have received increased attention in the field of hyperspectral image (HSI) denoising. Unfortunately, those methods usually analyze the prior structural information within different dimensions indiscriminately, ignoring the differences between modes, leaving substantial room for improvement. In this article, we first consider the low-rank properties in the subspace and prove that the structure correlation across the nonlocal self-similarity mode is much stronger than in the spatial sparsity and spectral correlation modes. On that basis, we introduce a new multidirectional low-rank regularization, in which each mode is assigned a different weight to characterize its contribution to estimating the tensor rank. After that, integrating the proposed regularization with the subspace-based tensor recovery framework, an optimization model for HSI mixed noise removal is developed. The proposed model can be addressed efficiently via the alternating minimization algorithm. Extensive experiments implemented with synthetic and real data demonstrate that the proposed method significantly outperforms other state-of-the-art HSI denoising methods, which clearly indicates the effectiveness of the proposed approach in HSI denoising.

scWMC: weighted matrix completion-based imputation of scRNA-seq data via prior subspace information.

MOTIVATION: Single-cell RNA sequencing (scRNA-seq) can provide insight into gene expression patterns at the resolution of individual cells, which offers new opportunities to study the behavior of different cell types. However, it is often plagued by dropout events, a phenomenon where the expression value of a gene tends to be measured as zero in the expression matrix due to various technical defects. RESULTS: In this article, we argue that borrowing gene and cell information across column and row subspaces directly results in suboptimal solutions due to the noise contamination in imputing dropout values. Thus, to impute more precisely the dropout events in scRNA-seq data, we develop a regularization for leveraging that imperfect prior information to estimate the true underlying prior subspace and then embed it in a typical low-rank matrix completion-based framework, named scWMC. To evaluate the performance of the proposed method, we conduct comprehensive experiments on simulated and real scRNA-seq data. Extensive data analysis, including simulated analysis, cell clustering, differential expression analysis, functional genomic analysis, cell trajectory inference and scalability analysis, demonstrate that our method produces improved imputation results compared to competing methods that benefits subsequent downstream analysis. AVAILABILITY AND IMPLEMENTATION: The source code is available at https://github.com/XuYuanchi/scWMC and test data is available at https://doi.org/10.5281/zenodo.6832477. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

scEFSC: Accurate single-cell RNA-seq data analysis via ensemble consensus clustering based on multiple feature selections.

With the development of next-generation sequencing technologies, single-cell RNA sequencing (scRNA-seq) has become one indispensable tool to reveal the wide heterogeneity between cells. Clustering is a fundamental task in this analysis to disclose the transcriptomic profiles of single cells and is one of the key computational problems that has received widespread attention. Recently, many clustering algorithms have been developed for the scRNA-seq data. Nevertheless, the computational models often suffer from realistic restrictions such as numerical instability, high dimensionality and computational scalability. Moreover, the accumulating cell numbers and high dropout rates bring a huge computational challenge to the analysis. To address these limitations, we first provide a systematic and extensive performance evaluation of four feature selection methods and nine scRNA-seq clustering algorithms on fourteen real single-cell RNA-seq datasets. Based on this, we then propose an accurate single-cell data analysis via Ensemble Feature Selection based Clustering, called scEFSC. Indeed, the algorithm employs several unsupervised feature selections to remove genes that do not contribute significantly to the scRNA-seq data. After that, different single-cell RNA-seq clustering algorithms are proposed to cluster the data filtered by multiple unsupervised feature selections, and then the clustering results are combined using weighted-based meta-clustering. We applied scEFSC to the fourteen real single-cell RNA-seq datasets and the experimental results demonstrated that our proposed scEFSC outperformed the other scRNA-seq clustering algorithms with several evaluation metrics. In addition, we established the biological interpretability of scEFSC by carrying out differential gene expression analysis, gene ontology enrichment and KEGG analysis. scEFSC is available at https://github.com/Conan-Bian/scEFSC.