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Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool for investigating cellular biology at an unprecedented resolution, enabling the characterization of cellular heterogeneity, identification of rare but significant cell types, and exploration of cell–cell communications and interactions. Its broad applications span both basic and clinical research domains. In this comprehensive review, we survey the current landscape of scRNA-seq analysis methods and tools, focusing on count modeling, cell-type annotation, data integration, including spatial transcriptomics, and the inference of cell–cell communication. We review the challenges encountered in scRNA-seq analysis, including issues of sparsity or low expression, reliability of cell annotation, and assumptions in data integration, and discuss the potential impact of suboptimal clustering and differential expression analysis tools on downstream analyses, particularly in identifying cell subpopulations. Finally, we discuss recent advancements and future directions for enhancing scRNA-seq analysis. Specifically, we highlight the development of novel tools for annotating single-cell data, integrating and interpreting multimodal datasets covering transcriptomics, epigenomics, and proteomics, and inferring cellular communication networks. By elucidating the latest progress and innovation, we provide a comprehensive overview of the rapidly advancing field of scRNA-seq analysis.

We present Model-based AnalysEs of Transcriptome and RegulOme (MAESTRO), a comprehensive open-source computational workflow ( http://github.com/liulab-dfci/MAESTRO ) for the integrative analyses of single-cell RNA-seq (scRNA-seq) and ATAC-seq (scATAC-seq) data from multiple platforms. MAESTRO provides functions for pre-processing, alignment, quality control, expression and chromatin accessibility quantification, clustering, differential analysis, and annotation. By modeling gene regulatory potential from chromatin accessibilities at the single-cell level, MAESTRO outperforms the existing methods for integrating the cell clusters between scRNA-seq and scATAC-seq. Furthermore, MAESTRO supports automatic cell-type annotation using predefined cell type marker genes and identifies driver regulators from differential scRNA-seq genes and scATAC-seq peaks.

Single-cell RNA sequencing (scRNA-seq) enables detailed profiling of cellular heterogeneity, but complex workflows and diverse data formats limit accessibility for clinicians and researchers without programming expertise. We introduce ShinySC, an R/Shiny-based desktop application designed to streamline comprehensive scRNA-seq analysis through an intuitive graphical interface. ShinySC supports various input formats, including 10x Genomics, Seurat, Scanpy, BD Rhapsody, and CellView. The tool integrates essential analytical procedures such as quality control, normalization, dimensionality reduction, clustering, marker gene identification, batch correction, differential expression analysis, and trajectory inference. Notably, ShinySC implements multiple automatic cell-type annotation methods-reference-based (SingleR), marker-based (ScType, scCATCH), and GPT-based (GPTCelltype)-with features for side-by-side comparison and manual label refinement. Benchmarking indicates robust performance for datasets containing up to 200,000 cells on standard desktop systems with 64 GB RAM, with analysis duration dependent on specific tasks and annotation methods. Demonstrative analyses of PBMC and interferon-stimulated datasets confirm ShinySC's efficacy in accurately annotating cell types and capturing condition-specific transcriptional dynamics. ShinySC provides a unified, user-friendly, and scalable platform for scRNA-seq analysis explicitly tailored for non-programming users. It surpasses existing limitations by accommodating multiple data formats, employing versatile annotation strategies, and generating high-quality, publication-ready figures. Available freely across Windows, macOS, and Linux platforms, ShinySC enhances the accessibility and reproducibility of single-cell transcriptomic research. http://tardis.cgu.edu.tw/ShinySC.

Abstract The prevalence of single-cell multi-omics datasets calls for automated cell type annotation methods that can characterize novel cell states. We developed $$\\Phi$$ Φ -Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. We adopt a highly versatile modeling strategy to characterize query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualizations, clustering, and cell type labeling. $$\\Phi$$ Φ -Space is applicable to a wide range analytical tasks beyond cell type transfer. Its ability to model complex phenotypic variations will facilitate biological discoveries from different omics types.

Background The advancement of single-cell sequencing has progressed our ability to solve biological questions. Cell type annotation is of vital importance to this process, allowing for the analysis and interpretation of enormous single-cell datasets. At present, however, manual cell annotation which is the predominant approach remains limited by both speed and the requirement of expert knowledge. Methods To address these challenges, we constructed a hierarchically organized marker map through manually curating over 26,000 cell marker entries from about 7000 publications. We then developed WISE, a weighted and integrated gene set enrichment method, to integrate the prevalence of canonical markers and ordered differentially expressed genes of specific cell types in the marker map. Benchmarking analysis suggested that our method outperformed state-of-the-art methods. Results By integrating the marker map and WISE, we developed a user-friendly and convenient web server, ACT ( http://xteam.xbio.top/ACT/ or http://biocc.hrbmu.edu.cn/ACT/ ), which only takes a simple list of upregulated genes as input and provides interactive hierarchy maps, together with well-designed charts and statistical information, to accelerate the assignment of cell identities and made the results comparable to expert manual annotation. Besides, a pan-tissue marker map was constructed to assist in cell assignments in less-studied tissues. Applying ACT to three case studies showed that all cell clusters were quickly and accurately annotated, and multi-level and more refined cell types were identified. Conclusions We developed a knowledge-based resource and a corresponding method, together with an intuitive graphical web interface, for cell type annotation. We believe that ACT, emerging as a powerful tool for cell type annotation, would be widely used in single-cell research and considerably accelerate the process of cell type identification.

Background Imaging-based spatial transcriptomics technologies allow us to explore spatial gene expression profiles at the cellular level. Cell type annotation of imaging-based spatial data is challenging due to the small gene panel, but it is a crucial step for downstream analyses. Many good reference-based cell type annotation tools have been developed for single-cell RNA sequencing and sequencing-based spatial transcriptomics data. However, the performance of the reference-based cell type annotation tools on imaging-based spatial transcriptomics data has not been well studied yet. Results We compared performance of five reference-based methods ( SingleR , Azimuth , RCTD , scPred and scmapCell ) with the marker-gene-based manual annotation method on an imaging-based Xenium data of human breast cancer. A practical workflow has been demonstrated for preparing a high-quality single-cell RNA reference, evaluating the accuracy, and estimating the running time for reference-based cell type annotation tools. Conclusions SingleR was the best performing reference-based cell type annotation tool for the Xenium platform, being fast, accurate and easy to use, with results closely matching those of manual annotation.

Background Single-cell RNA-sequencing (scRNA-seq) has become a widely used tool for both basic and translational biomedical research. In scRNA-seq data analysis, cell type annotation is an essential but challenging step. In the past few years, several annotation tools have been developed. These methods require either labeled training/reference datasets, which are not always available, or a list of predefined cell subset markers, which are subject to biases. Thus, a user-friendly and precise annotation tool is still critically needed. Results We curated a comprehensive cell marker database named scMayoMapDatabase and developed a companion R package scMayoMap, an easy-to-use single-cell annotation tool, to provide fast and accurate cell type annotation. The effectiveness of scMayoMap was demonstrated in 48 independent scRNA-seq datasets across different platforms and tissues. Additionally, the scMayoMapDatabase can be integrated with other tools and further improve their performance. Conclusions scMayoMap and scMayoMapDatabase will help investigators to define the cell types in their scRNA-seq data in a streamlined and user-friendly way.

Single-cell technologies have revolutionized our ability to study cellular heterogeneity and dynamics at unprecedented resolutions. In this fast-growing field, it becomes increasingly challenging to navigate the vast number of tools and steps for analysis. It is particularly difficult to integrate and analyze large datasets that require extensive collaborations and customized pipelines to obtain robust results. We present CytoAnalyst, a web-based platform that offers a number of important advantages over existing tools for single-cell analysis. First, the platform enables custom pipeline configuration using an efficient study management system and a broad range of analysis modules. Second, the platform supports parallel analysis instances, facilitating the comprehensive comparison of different methods or parameter settings available at each analysis step. Third, the advanced sharing system facilitates real-time synchronization among team members and seamless analysis continuation across different devices. Finally, the grid-layout visualization system supports simultaneous displays of different data aspects, allowing for the comparison of multiple labels and plots side-by-side for comprehensive data insights, with the ability to save and reload visualization settings at any analysis step. The platform incorporates multiple blending modes, allowing users to combine plots in various ways for comprehensive data exploration. CytoAnalyst supports a high level of analytical rigor while providing user-friendly and flexible operations through its carefully designed interface and extensive documentation. The platform supports all major web browsers and is freely available at https://cytoanalyst.tinnguyen-lab.com .

Single-cell RNA sequencing (scRNA-seq) provides a high-resolution view of cellular heterogeneity, yet accurate cell-type annotation remains challenging due to data sparsity, technical noise, and variability across tissues, platforms, and species. Many existing annotation tools depend on a single form of prior knowledge, such as marker genes or reference profiles, which can limit performance when these resources are incomplete or inconsistent. Here, we present scANMF, a prior- and graph-regularized non-negative matrix factorization framework that integrates marker-gene information, partial label supervision, and the local manifold structure into a unified annotation model. scANMF factorizes the expression matrix into interpretable gene–factor and cell–factor representations, enabling accurate annotation in settings with limited or noisy prior information. Across multiple real scRNA-seq collections, scANMF achieved a high annotation accuracy in within-dataset, cross-platform, and cross-species evaluations. The method remained stable under varying levels of label sparsity and marker-gene noise and showed a broad robustness to hyperparameter choices. Ablation analyses indicated that marker priors, label supervision, and graph regularization contribute complementary information to the overall performance. These results support scANMF as a practical and robust framework for single-cell annotation, particularly in applications where high-quality prior knowledge is restricted.

The prevalence of single-cell multi-omics datasets calls for automated cell type annotation methods that can characterize novel cell states. We developed Φ -Space, a computational framework for the continuous phenotyping of single-cell multi-omics data. We adopt a highly versatile modeling strategy to characterize query cell identity in a low-dimensional phenotype space, defined by reference phenotypes. The phenotype space embedding enables various downstream analyses, including insightful visualizations, clustering, and cell type labeling. Φ -Space is applicable to a wide range analytical tasks beyond cell type transfer. Its ability to model complex phenotypic variations will facilitate biological discoveries from different omics types.