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Review
. 2024 Jun 10;44(1):50.
doi: 10.1007/s10571-024-01484-3.

A Bibliometric Analysis of the Spatial Transcriptomics Literature from 2006 to 2023

Shu-Han Zhao #  1   2 , Xin-Yu Ji #  3 , Guo-Zhen Yuan  1 , Tao Cheng  1 , Hai-Yi Liang  2 , Si-Qi Liu  2 , Fu-Yi Yang  2 , Yang Tang  4 , Shuai Shi  5
Affiliations
Review

A Bibliometric Analysis of the Spatial Transcriptomics Literature from 2006 to 2023

Shu-Han Zhao et al. Cell Mol Neurobiol. .

Abstract

In recent years, spatial transcriptomics (ST) research has become a popular field of study and has shown great potential in medicine. However, there are few bibliometric analyses in this field. Thus, in this study, we aimed to find and analyze the frontiers and trends of this medical research field based on the available literature. A computerized search was applied to the WoSCC (Web of Science Core Collection) Database for literature published from 2006 to 2023. Complete records of all literature and cited references were extracted and screened. The bibliometric analysis and visualization were performed using CiteSpace, VOSviewer, Bibliometrix R Package software, and Scimago Graphica. A total of 1467 papers and reviews were included. The analysis revealed that the ST publication and citation results have shown a rapid upward trend over the last 3 years. Nature Communications and Nature were the most productive and most co-cited journals, respectively. In the comprehensive global collaborative network, the United States is the country with the most organizations and publications, followed closely by China and the United Kingdom. The author Joakim Lundeberg published the most cited paper, while Patrik L. Ståhl ranked first among co-cited authors. The hot topics in ST are tissue recognition, cancer, heterogeneity, immunotherapy, differentiation, and models. ST technologies have greatly contributed to in-depth research in medical fields such as oncology and neuroscience, opening up new possibilities for the diagnosis and treatment of diseases. Moreover, artificial intelligence and big data drive additional development in ST fields.

Keywords: Bibliometric analysis; Cancer; Differentiation; Models; Spatial transcriptomics; Tissue recognition.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig. 1
Fig. 1
Flowchart for spatial transcriptomics bibliographic analysis. TS Topics
Fig. 2
Fig. 2
The annual publication and annual average citation trends from 2006 to 2023. A The annual publication. B The annual average citation trends
Fig. 3
Fig. 3
The network visualization maps of A citing and B co-cited journals
Fig. 4
Fig. 4
Collaboration networks among countries and among institutions. A The collaboration map among countries. B The cooperation network among the top 20 most productive countries. C The time-overlay map of the cooperation network among the top 20 most productive countries. D The cooperation network among the top 50 most productive institutions. E The time-overlay map of the cooperation network among the top 50 most productive institutions
Fig. 5
Fig. 5
Scholar cooperation maps. A The cooperation network among the top 100 most productive authors. B Time-overlay maps of the top 100 most productive authors. Each node represents an author, and each line represents the link between two authors. C The spectral density map of the top 100 most productive authors. D The cooperation network among the top 100 most productive co-cited authors. E The spectral density maps of the top 100 most co-cited authors
Fig. 6
Fig. 6
Knowledge map of keywords. A The cooperation network among the top 100 keywords. B Time-overlay maps of the top 100 keywords. C Cluster diagrams of keywords. D The spectral density maps of the top 100 keywords
Fig. 7
Fig. 7
Top 22 keywords with the strongest citation burst
See this image and copyright information in PMC

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