Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study
April 18th, 2025
This bibliometric analysis of single-cell sequencing in cancer research indicates that China and the USA produce significantly more scholarly articles than other nations. Burst detection identifies emerging terms such as 'intra-tumor heterogeneity,' 'clonal evolution,' and 'drug delivery systems,' which are expected to influence future research.
Our study examines the developmental trends in single-cell sequencing within the domain of cancer. The results provide researchers, clinicians, and policymakers with an in-depth overview of the current state of knowledge and the understanding in this field. Our bibliometric analysis shows China and US leading single-cell sequencing in cancer research. Emerging terms like intratumoral heterogeneity, clone evolution, and drug delivery systems suggest future research directions.
Our study aims to evaluate the current landscape of research, identify significant research hot spots, and elucidate emerging trends in order to provide actionable insights for researchers and policymakers.
[Narrator] To begin, access the Web of Science Core Collection database from their website. Construct a search strategy using targeted keywords, such as single-cell sequencing and cancer, to identify relevant literature. Then, search for the results. Set the search parameters by selecting the publication period from January 1st, 2010 to December 31st, 2023. Choose English as the language and select Article and Review as the document types. Save the compiled data as plain text files for subsequent bibliometric analysis and compile the selected publications in Full Record and Cited References format. Now, launch the Biblioshiny interface from the bibliometric package in R. Import the plain text files and export the data set in R data format for analysis. Create a bar plot to show the annual number of publications and overlay a line graph to depict citation counts over time. Extract institutional data from the Affiliations section using the Analyze Results feature in the Web of Science Core Collection database. Use the ggplot2 package in R to generate a horizontal bar chart visualizing institutional publication counts. Using VOSviewer, conduct co-authorship analysis by selecting Create from the main menu and choosing Create a map based on bibliographic data. Import the plain text files and set the analysis type to Co-authorship. Select Full counting and choose Organizations as the unit of analysis. Click on Finish to generate the visualization. Access the Researcher Profiles section in the Web of Science Core Collection database to identify and rank authors based on the total number of published articles. Use the ggplot2 package in R to generate a horizontal bar chart, visualizing the top authors by publication count. Now, retrieve detailed metrics, including the country, affiliated institution, and H index for the top 10 authors using the Web of Science Core Collection data. For author collaboration networks, launch VOSviewer and select the Create button, followed by Create a map based on bibliographic data. Import the relevant plain text files by selecting Read data from bibliographic database files. Set the analysis type to Co-authorship in VOSviewer. Use the Full counting method and select Authors as the unit of analysis. Click on Finish to generate an overlay visualization. Use the hindex function in R to calculate journal metrics for the identified publication sources. For knowledge flow analysis, open CiteSpace and navigate to the Data menu. Select WOS to import the data set for knowledge flow analysis. With CiteSpace, select Overlay Maps and JCR Journals Maps options for map configuration with 0 z-score parameter and adjust the background color. Open CiteSpace, navigate to the Data menu, and select the data to begin co-cited reference analysis. Set the node type to Reference. After enabling Pathfinder and pruning sliced networks features, execute the analysis. To identify high-impact references, select the Burstness option in CiteSpace and set the gamma parameter to a range from 0 to 1. For keyword co-occurrence, conduct keyword frequency analysis in R using the bibliometric package. Set the analysis field to Author's keywords and restrict the output to the top 20 keywords. For keyword burst analysis, use CiteSpace with the burstness enabled. Set the gamma parameter to 0 to 1. Next, open VOSviewer and choose Create, then select Create a map based on bibliographic data. Use the Read data from bibliographic database files option and import the plain text files. Set the analysis type to Co-occurrence, use the Full counting method, and select Author keywords as the unit of analysis. Apply a minimum threshold of 40 keyword occurrences and click on Finish to generate the co-occurrence network. The annual number of publications in single-cell sequencing in cancer research showed a sharp increase after 2016 with notable citation peaks in 2014 and 2020. China and the United States emerged as the top contributors, with China exhibiting the fastest growth in publication output. Harvard University was identified as the leading institution in terms of publication count, followed by several major Chinese universities. Frontiers in Immunology published the highest number of articles, while journals like Cell and Nature had the most citations. Articles by Macosko et al. and Tirosh et al. had the strongest citation bursts, indicating major breakthroughs in technique and analysis. Immune microenvironment was the most frequently used keyword with emerging trends like lung adenocarcinoma and cancer-associated fibroblasts gaining recent prominence. Tumor evolution showed the longest citation burst duration.
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Overview
This bibliometric analysis examines the trends in single-cell sequencing within cancer research, highlighting the dominance of China and the USA in scholarly output. It identifies emerging terms that are likely to shape future research directions.
Key Study Components
Area of Science
- Single-cell sequencing
- Cancer research
- Bibliometric analysis
Background
- Single-cell sequencing is a pivotal technique in understanding cancer biology.
- Research output varies significantly across countries.
- Identifying emerging trends can guide future investigations.
- Intra-tumor heterogeneity and clonal evolution are critical concepts in cancer research.
Purpose of Study
- To evaluate the current landscape of single-cell sequencing in cancer.
- To identify significant research hotspots.
- To provide actionable insights for researchers and policymakers.
Methods Used
- Bibliometric analysis of published articles.
- Trend analysis of emerging research terms.
- Comparative analysis between countries.
- Identification of future research directions.
Main Results
- China and the USA lead in single-cell sequencing publications.
- Emerging terms include intra-tumor heterogeneity and clonal evolution.
- Research trends indicate a focus on drug delivery systems.
- Insights provided can influence future research agendas.
Conclusions
- The study highlights the importance of single-cell sequencing in cancer research.
- Identifying emerging trends is crucial for guiding future studies.
- Collaboration between researchers and policymakers is essential for advancing the field.
Frequently Asked Questions
Executive Industry Relevance
Single-cell sequencing is transforming oncology discovery by enabling high-resolution analysis of tumor heterogeneity and clonal evolution. This bibliometric study reveals global research momentum, highlighting the strategic importance of integrative analytics and international collaboration for advancing cancer target validation and translational research. The rapid expansion of this field positions single-cell sequencing as a foundational capability for next-generation oncology pipelines.
Strategic Applications in Biopharma R&D
Early Discovery & Target Validation
- Enables precise dissection of intra-tumor heterogeneity for functional target validation.
- Supports mechanistic de-risking by clarifying clonal evolution and cellular diversity.
- Facilitates identification of novel biomarkers and actionable pathways in cancer.
- Drives predictive confidence in early-stage oncology asset selection.
Screening & Assay Development
- Provides validated single-cell data for developing robust, quantitative assays.
- Enables reproducible profiling of immune microenvironments and tumor subpopulations.
- Supports standardization of screening platforms for immunotherapy and drug delivery research.
- Accelerates readiness for high-throughput compound evaluation in disease-relevant systems.
Translational & Preclinical Research
- Aligns preclinical models with human tumor complexity through single-cell resolution data.
- Enables translational biomarker discovery for patient stratification and response prediction.
- Supports risk-adjusted advancement decisions by linking discovery findings to clinical relevance.
- Strengthens continuity from discovery through preclinical validation in oncology portfolios.
Pipeline & Workflow Integration
Single-cell sequencing integrates across the oncology discovery continuum, from early hypothesis testing to translational biomarker development and preclinical model validation.
- Discovery Biology: Advances hypothesis-driven interrogation of tumor evolution and immune microenvironment.
- Screening: Delivers quantitative, reproducible single-cell readouts for assay development.
- Analytics: Provides high-dimensional data for comparative analysis of gene expression and epigenetic states.
- Translational Research: Bridges discovery insights to preclinical and clinical biomarker strategies.
- Enterprise Reuse: Establishes a scalable, reusable platform for ongoing oncology research and portfolio expansion.
Operational & Enterprise Impact
- Scientific Value: Increases predictive confidence and reduces mechanistic ambiguity in oncology R&D.
- Operational Value: Enhances standardization, reproducibility, and scalability of single-cell analytics.
- Strategic Value: Informs go/no-go decisions and optimizes capital allocation in cancer pipelines.
- Portfolio Impact: Enables risk-adjusted prioritization and advancement of high-value oncology assets.
Implementation Considerations
- Requires expertise in single-cell genomics, bioinformatics, and oncology biology.
- Demands advanced instrumentation and integrative analytical infrastructure.
- Necessitates cross-team standardization for data generation and interpretation.
- Must address adaptation across diverse tumor types and model systems.
- Data complexity and integration remain practical challenges for enterprise-scale adoption.
