Abstract Purpose This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric analysis and semantic mining. Design/methodology/approach The literatures on single cell research were extracted from Clarivate Analytic's Web of Science Core Collection between 2009 and 2019. Firstly, bibliometric analyses were performed with Thomson Data Analyzer (TDA). Secondly, topic identification and evolution trends of single cell research was conducted through the LDA topic model. Thirdly, taking the post-discretized method which is used for topic evolution analysis for reference, the topics were also be dispersed to countries to detect the spatial distribution. Findings The publication of single cell research shows significantly increasing tendency in the last decade. The topics of single cell research field can be divided into three categories, which respectively refers to single cell research methods, mechanism of biological process, and clinical application of single cell technologies. The different trends of these categories indicate that technological innovation drives the development of applied research. The continuous and rapid growth of the topic strength in the field of cancer diagnosis and treatment indicates that this research topic has received extensive attention in recent years. The topic distributions of some countries are relatively balanced, while for the other countries, several topics show significant superiority. Research limitations The analyzed data of this study only contain those were included in the Web of Science Core Collection. Practical implications This study provides insights into the research progress regarding single cell field and identifies the most concerned topics which reflect potential opportunities and challenges. The national topic distribution analysis based on the post-discretized analysis method extends topic analysis from time dimension to space dimension. Originality/value This paper combines bibliometric analysis and LDA model to analyze the evolution trends of single cell research field. The method of extending post-discretized analysis from time dimension to space dimension is distinctive and insightful.

中文翻译：

---

基于文献计量分析和LDA模型的全球单细胞研究趋势概述（2009-2019）

摘要 目的本文旨在从文献计量分析和语义挖掘的角度描述单细胞研究的全球研究概况和发展趋势。设计/方法/途径 单细胞研究的文献提取自科睿唯安的 Web of Science 核心合集中 2009 年至 2019 年。首先，使用 Thomson Data Analyzer (TDA) 进行文献计量分析。其次，通过LDA主题模型进行单细胞研究的主题识别和演化趋势。第三，借鉴用于主题演化分析的后离散化方法，也将主题分散到各国以检测空间分布。研究结果 单细胞研究的发表在过去十年中显示出显着增加的趋势。单细胞研究领域的课题可以分为三类，分别是单细胞研究方法、生物过程机理和单细胞技术的临床应用。这些类别的不同趋势表明，技术创新推动了应用研究的发展。癌症诊疗领域的课题强度持续快速增长，表明该研究课题近年来受到广泛关注。一些国家的主题分布比较均衡，而另一些国家则有几个主题表现出明显的优势。研究限制 本研究的分析数据仅包含 Web of Science 核心合集中包含的数据。实际意义 本研究提供了有关单细胞领域研究进展的见解，并确定了反映潜在机遇和挑战的最受关注的主题。基于后离散化分析方法的全国话题分布分析将话题分析从时间维度扩展到空间维度。原创性/价值本文结合文献计量分析和LDA模型分析单细胞研究领域的演变趋势。