scEpiTools: a database to comprehensively interrogate analytic tools for single-cell epigenomic data

Abstract Single-cell sequencing technology has enabled the characterization of cellular heterogeneity at an unprecedented resolution. To analyze single-cell RNA-sequencing data, numerous tools have been proposed for various analytic tasks, which have been systematically summarized and concluded in a comprehensive database called scRNA-tools. Although single-cell epigenomic data can effectively reveal the chromatin regulatory landscape that governs transcription, the analysis of single-cell epigenomic data presents assay-specific challenges, and an abundance of tools with varying types and functionalities have thus been developed. Nevertheless, these tools have not been well summarized, hindering retrieval, selection, and utilization of appropriate tools for specific analyses. To address the issues, we here proposed scEpiTools database with a multi-functional platform ( http://health.tsinghua.edu.cn/scepitools ). Specifically, based on the comprehensive collection and detailed annotation of 553 articles, scEpiTools groups articles into 14 major categories and 90 subcategories, provides task-specific recommendation for different emphases, and offers intuitive trend analysis via directed graphs, word clouds, and statistical distributions. For single-cell chromatin accessibility data analysis, we proposed a novel ensemble method named scEpiEnsemble, which, along with multiple methods as built-in kernels, can be used for flexible and efficient online analysis via the scEpiTools platform. We envision that scEpiTools will guide tool usage and development for single-cell epigenomic data and provide valuable resources for understanding regulatory mechanisms and cellular identity. Author summary Compared to single-cell RNA-sequencing data, single-cell epigenomic data can reflect a set of epigenetic modifications at the cellular level. In general, the analysis of these data is typically divided into several steps: 1) retrieving available tools based on the omics of data and tasks; 2) selecting appropriate tools manually; and 3) utilizing the chosen tools to analyze data. However, due to the rapid development of tools and the unique complexity of the data, each of the above steps is extremely challenging for researchers. To provide researchers with great convenience, we developed scEpiTools ( http://health.tsinghua.edu.cn/scepitools ), a database with multiple functionalities. For instance, given the omics type and the analytic task, researchers can easily browse all the available tools via the hierarchical categorization of scEpiTools, and get recommendation scores from multiple perspectives. Considering that researchers may encounter difficulties in hardware requirements or environment setup, we also provide online analysis with various commonly used tools, as well as a novel ensemble method named scEpiEnsemble. In summary, scEpiTools represents a valuable resource for the single-cell epigenomics community, facilitating retrieval, selection and utilization of appropriate tools for diverse analyses, and helping to drive future advancements in the field.