<abstract xmlns="http://www.w3.org/1999/xhtml">

<sec><h3>Purpose</h3>
<p>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.</p>
</sec>
<sec><h3>Design/methodology/approach</h3>
<p>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.</p>
</sec>
<sec><h3>Findings</h3>
<p>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 <italic>cancer diagnosis and treatment</italic> 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.</p>
</sec>
<sec><h3>Research limitations</h3>
<p>The analyzed data of this study only contain those were included in the Web of Science Core Collection.</p>
</sec>
<sec><h3>Practical implications</h3>
<p>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.</p>
</sec>
<sec><h3>Originality/value</h3>
<p>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.</p>
</sec>
</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.

Overview of Trends in Global Single Cell Research Based on Bibliometric Analysis and LDA Model (2009–2019)

Journal of Data and Information Science

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Purpose
This article aims to describe the global research profile and the development trends of single cell research from the perspective of bibliometric...

Overview of Trends in Global Single Cell Research Based on Bibliometric Analysis and LDA Model (2009–2019)

Article Category: Research Paper

Pubblicato online: 27 nov 2020

Pagine: 163 - 178

Ricevuto: 08 set 2020

Accettato: 23 ott 2020

DOI: https://doi.org/10.2478/jdis-2021-0008

Parole chiave
LDA model, Topic evolution, Bibliometric analysis, Post-discretized, Single-cell

© 2021 Tian Jiang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Overview of Trends in Global Single Cell Research Based on Bibliometric Analysis and LDA Model (2009–2019)

Article Category: Research Paper

Pubblicato online: 27 nov 2020

Pagine: 163 - 178

Ricevuto: 08 set 2020

Accettato: 23 ott 2020

DOI: https://doi.org/10.2478/jdis-2021-0008

Parole chiaveLDA model, Topic evolution, Bibliometric analysis, Post-discretized, Single-cell

© 2021 Tian Jiang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
LDA model, Topic evolution, Bibliometric analysis, Post-discretized, Single-cell