Method Article

Global and Current Research Trends of Single-Cell Sequencing in Cancer: A Bibliometric and Visualization Study

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DOI:

10.3791/67880

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April 18th, 2025

 ,  ,  ,  ,  ,  ,  , 
1Department of Operating Theatre, Tianjin Medical University General Hospital, 2Department of General Surgery, Tianjin Medical University General Hospital, Tianjin Medical University, 3Department of Medical Oncology, Tianjin Medical University General Hospital

* These authors contributed equally

In This Article

Summary

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.

Abstract

Cancer poses a significant challenge to human health due to its complex biological systems, necessitating in-depth analysis. Single-cell sequencing has become an essential tool for investigating these systems, enabling the detection of gene expression and epigenetic modifications at the single-cell level. To elucidate research trends, collaboration networks, and knowledge dissemination in this field, a bibliometric analysis was conducted using the Web of Science Core Collection database, covering publications from January 1, 2010, to December 31, 2023. The Bibliometrix package in R was used to extract and analyze key publication data, including document types, countries, institutions, authors, and keywords. Additionally, CiteSpace, VOSviewer, and the Online Analysis Platform of Literature Metrology were employed for data compilation and visualization. The analysis identified 34,074 authors from 3,129 institutions across 75 countries and regions, contributing to 5,680 publications on single-cell sequencing in cancer, published in 788 academic journals. China and the United States emerged as the leading nations in publication volume. Harvard University produced the highest number of publications (320), with Aviv Regev, affiliated with Harvard, recognized as a key contributor. Leading journals, such as Frontiers in Immunology and Nature Communications, highlight both established and emerging research areas, including the immune microenvironment and immunotherapy. Key trends and potential areas for future research include intra-tumor heterogeneity, clonal evolution, and drug delivery systems. This study provides a comprehensive overview of single-cell sequencing research in oncology, emphasizing its rapid progress, driven by technological advancements and international collaborations. Strengthening global partnerships, developing integrative analytical tools, and addressing data complexities will be crucial for advancing personalized cancer therapies and deepening insights into cancer biology.

Introduction

Cancer represents one of the most detrimental diseases, ranking as the second leading cause of mortality worldwide1. It is estimated that by 2035, approximately a quarter of the global population will be directly impacted by cancer2,3. The pathogenesis of cancer is primarily linked to dysregulation in cellular growth, which is influenced by a variety of tumorigenic factors4,5. The "Hallmarks of Cancer" were conceptualized as a set of functional capabilities that facilitate the transition from normal cellular states to neoplastic growth, specifically those capabilities essential for the formation of malignant tumors6. Sequencing technology plays a pivotal role in advancing our understanding of disease pathogenesis. However, due to the inherent heterogeneity of tumors, identifying the genomic characteristics of low-abundance stem cells through high-throughput sequencing analysis of tumor tissues presents significant challenges7,8.

Single-cell sequencing, which includes genomics, transcriptomics, epigenomics, proteomics, and metabolomics, represents a powerful methodological approach for elucidating cellular and molecular landscapes at the single-cell level9,10. Its application in cancer research has significantly enhanced the understanding of the biological characteristics and dynamics present within neoplastic lesions, thereby facilitating a more comprehensive comprehension of cancer development and metastasis.

The bibliometric analysis examines the structural characteristics and attributes of scholarly publications and has been widely employed in both qualitative and quantitative assessments of scientific literature11,12. By comparing contributions from various countries, institutions, researchers, and publications, it is possible to elucidate and anticipate potential advancements within a particular research domain. Although there has been a substantial increase in systematic and narrative reviews focusing on single-cell sequencing research in cancer, there remains a notable deficiency in comprehensive analyses within the realm of quantitative assessment13,14,15. This study aims to conduct a comprehensive analysis of the developmental trends and prominent research topics in single-cell sequencing within the domain of cancer, utilizing bibliometric methods. The findings will offer researchers, clinicians, and policymakers a detailed overview of the current state of knowledge and understanding in this area.

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Protocol

The data used in this study was obtained from the Web of Science Core Collection (2010-2023).

1. Data collection

  1. Database selection
    1. Access the Web of Science Core Collection (WoSCC) database via. https://webofscience. clarivate.cn/wos/author/author-search. 
    2. Construct a search strategy using targeted keywords, specifically "single-cell sequencing" and "cancer," to identify relevant literature. Click the search button to complete the literature search.
      NOTE: Refer to Supplementary Table 1 for a complete list of keywords employed in the search strategy to enhance accuracy and inclusivity.
  2. Search parameters
    1. Choose the publication period from January 1, 2010, to December 31, 2023, to capture the most recent and comprehensive research trends.
    2. Select English as the language for the search results and choose Article and Review was the article types to ensure data consistency and facilitate comparative analysis.
    3. Ensure relevance by excluding publications outside the single-cell sequencing and cancer research domains.
  3. Data retrieval and format
    1. Compile the selected publications in Full Record and Cited References format to preserve detailed metadata.
    2. Save the collected data as Plain Text files for subsequent analysis using bibliometric tools.
    3. Verify that each record contains complete metadata, including citation and co-authorship information, to enable a thorough bibliometric analysis (Figure 1).

2. Data preprocessing

  1. Data collection and import
    1. Launch the bibliometrix package's biblioshiny interface in R.
      NOTE: The specific code is provided as follows:
      library(bibliometrix)
      packageVersion("bibliometrix")
      ​biblioshiny()
    2. Access the WoSCC database and select a merged Plain Text file containing bibliometric data.
    3. Import the data and export it in R Data Format for subsequent analysis.
  2. Annual growth trend of publications and citations
    1. Convert the PY (publication year) and Z9 (citation count) columns to numeric format.
    2. Group the data by publication year and calculate annual publication counts and total citations.
    3. Create a bar plot to represent the number of publications each year and overlay a line graph to illustrate citation counts over time.
      NOTE: The specific code is provided as Code 1 in Supplementary File 1.
  3. National publication and collaboration analysis
    1. National publication and collaboration aggregation
      1. Summarize the annual publications and citations by each country using the Year Published(PY) and Author Countries(AU_CO1) fields.
      2. Focus the analysis on the top 10 countries by publication volume.
    2. Metric computation
      1. Calculate key metrics, including the number of publications (NP), citation frequency (NC), single-country publications (SCP), and multiple-country publications (MCP)16.
      2. Determine the ratio of multiple-country publications (MCP_Ratio) as an indicator of international collaboration.
      3. Use R's H-index function to compute impact indices (H-index, G-index, and M-index) for each country over a 14-year period17.
        NOTE: The specific code is provided as Code 2 in Supplementary File 1. Use the online analysis platform of literature metrology (https://bibliometric.com/) to examine the collaborative relationships among countries. Upload data in Plain Text format from the WoSCC. Employ the "country relationships" feature to assess international collaborations. Culminate in a visualization of the collaboration network among the leading contributing countries.
  4. Institutional publication and collaboration analysis
    1. Data extraction and ranking
      1. Extract institutional data from the Affiations section within the Analyze Results feature of the WoSCC database.
      2. Rank institutions in descending order according to their total number of published articles.
    2. Data visualization
      1. Generate a horizontal bar chart using the ggplot2 package in R to illustrate the publication volume across leading institutions.
        NOTE: The specific code is provided as Code 3 in Supplementary File 1.
    3. Co-authorship analysis
      1. Analysis setup in VOSviewer: Launch VOSviewer, and then select Create from the main menu. Choose Create a map based on bibliographic data and opt for Read data from bibliographic database files. Finally, import the Plain Text files.
      2. Configuration and parameters: Set the analysis type to co-authorship in VOSviewer. Choose the Full counting method and select organizations as the unit of analysis. Configure the parameters to include a maximum of 1,200 organizations per document and set the minimum threshold to 30 documents per organization to ensure a comprehensive network analysis.
      3. Visualization and interpretation: Complete the setup by clicking on Finish to generate a visualization map that illustrates collaboration networks between institutions. Ensure that the map highlights the connectivity and partnership levels, identifying central hubs of research activity and key areas of cooperation among institutions.
  5. Analysis of authors and author collaboration
    1. Identification of prolific authors: Access the Researcher Profiles section in the WoSCC database to rank authors based on the total number of published articles.
    2. Data visualization: Use the ggplot2 package in R to create a horizontal bar chart, visually representing the publication volume of leading authors.
    3. Evaluation of author contributions: Retrieve additional metrics for the top 10 authors by publication volume, including the H-index, country, and affiliated institution, using data from the WoSCC database.
      NOTE: The specific code is provided as Code 4 in Supplementary File 1.
    4. Author collaboration networks
      1. Setting up analysis in VOSviewer: Launch VOSviewer and select the Create button, followed by Create a map based on bibliographic data. Import the relevant Plain Text files containing bibliographic data by selecting Read data from bibliographic database files.
      2. Configuration and parameters: Set the analysis type to co-authorship and utilize the Full counting method. Select authors as the unit of analysis. Adjust the parameters to include a maximum of 33 organizations per document and establish a minimum threshold of 15 documents per author to ensure meaningful collaboration data is captured.
      3. Visualization and insights: Click on Finish to generate an overlay visualization, which illustrates the temporal evolution of author collaborations, providing insight into the dynamics and growth of co-authorship networks over time.
  6. Analysis of journals and co-cited journals
    1. Calculation of journal metrics: Use the H-index function in R to calculate key metrics for each journal, including the number of publications per source (NP), citation frequency (NC), year of first publication (PY_start), and impact indices (H-index, G-index, and M-index).
    2. Retrieval of impact factors and rankings: Obtain journal impact factors and quartile rankings from the WoSCC database to further evaluate the influence and standing of each journal in the field.
      NOTE: The specific code is provided as Code 5 in Supplementary File 1.
    3. Knowledge flow analysis
      1. Data Import and Setup in CiteSpace: Open CiteSpace and navigate to the Data menu. Select Import/Export and choose WOS to import data. Configure the input and output paths as required.
      2. Map selection and configuration: Select Overlay Maps and JCR Journals Maps options within CiteSpace, setting the z-score to 0. This setup enables the generation of a dual-map to illustrate the flow of knowledge between journals.
  7. Analysis of co-cited references and clustering network
    1. Data import and setup in CiteSpace
      1. Open CiteSpace, navigate to the Data menu, and select Import/Export.
      2. Import data from the WoSCC database in plain text, configuring the analysis time slice from January 2010 to December 2023 with a one-year interval for temporal detail.
    2. Network configuration and pruning
      1. Set the node type to Reference in CiteSpace. Utilize the Pathfinder option and the Pruning Sliced Networks feature to apply pruning to the network.
      2. Execute the analysis by clicking on GO to generate a reference co-citation map, adjusting font and color settings for improved readability.
    3. Identifying high-impact references
      1. Select the Burstness option and set the parameter γ to [0, 1] in CiteSpace.
      2. Refresh the data to generate a list of the top 20 references with the strongest citation bursts.
  8. Analysis of keyword co-occurrence
    1. Keyword frequency analysis in R
      1. Conduct keyword analysis using the bibliometrix package in R, focusing on the most frequent keywords across documents.
      2. Set the field to Author's keywords and limit the number to the top 20 keywords, filtering out synonyms to ensure consistency.
    2. Keyword burst analysis in CiteSpace
      1. Use CiteSpace to perform keyword burst analysis by selecting the Burstness option and setting the parameter γ to [0, 1].
      2. Refresh the data to generate a list of the top 30 keywords with the strongest citation bursts.
    3. Keyword co-occurrence network analysis in VOSviewer
      1. Open VOSviewer, select Create, and then Create a map based on bibliographic data.
      2. Import the plain text files using the Read data from bibliographic database files option.
      3. Set the analysis type to co-occurrence, using the Full counting method, with author keywords as the unit of analysis.
      4. Apply a minimum threshold of 40 keyword occurrences and click on Finish to complete the analysis, producing a co-occurrence network that visualizes keyword relationships within the field.

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Results

Annual growth trend of publications and citations
From 2010 to 2023, a total of 6,767 publications related to single-cell sequencing in cancer were identified in the WoSCC database. A total of 602 studies published between 2010 and 2023 were excluded from the analysis, followed by the exclusion of five studies not published in English. Additionally, 480 articles were excluded based on predefined exclusion criteria, comprising 361 meeting abstracts, 83 editorial materials, and 36 articles classified u...

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Discussion

Bibliometric analysis serves as a quantitative approach to evaluating the characteristics and scholarly impact of significant publications26. This study conducted an extensive bibliometric analysis of 5,680 articles related to single-cell sequencing in cancer research, extracted from the WoSCC database and published between 2010 and 2023. This analysis aimed to assess the current state of research, identify key research hotspots, and elucidate emerging trends to provide actionable insights for res...

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Disclosures

The authors have nothing to disclose.

Materials

List of materials used in this article
NameCompanyCatalog NumberComments
bibliometrix packageComprehensive R Archive Network (CRAN)bibliometrix 4.3.0A forest plot that allows for multiple confidence intervals per row, custom fonts for each text element, custom confidence intervals, text mixed with expressions, and more.
CiteSpaceChaomei Chen, Drexel UniversityCiteSpace 6.2.R4 (64-bit) beta Basic‌CiteSpace‌ is a scientific literature analysis tool. Its main function is to analyze the underlying knowledge in scientific literature through visual means, showing the structure, rules and distribution of scientific knowledge. The main functions of CiteSpace include: research collaboration analysis ‌, important journal judgment ‌, core topic mining and so on.
dplyrComprehensive R Archive Network (CRAN)dplyr 1.1.4dbplyr is the database backend for dplyr. It allows you to use remote database tables as if they are in-memory data frames by automatically converting dplyr code into SQL.
esquisseComprehensive R Archive Network (CRAN)esquisse 2.0.1This addin allows you to interactively explore your data by visualizing it with the ggplot2 package. It allows you to draw bar plots, curves, scatter plots, histograms, boxplot and sf objects, then export the graph or retrieve the code to reproduce the graph.
forcatsComprehensive R Archive Network (CRAN)forcats 1.0.0R uses factors to handle categorical variables, variables that have a fixed and known set of possible values. Factors are also helpful for reordering character vectors to improve display. The goal of the forcats package is to provide a suite of tools that solve common problems with factors, including changing the order of levels or the values. 
ggplot2Comprehensive R Archive Network (CRAN)ggplot2 3.5.1ggplot2 is a system for declaratively creating graphics, based on The Grammar of Graphics. You provide the data, tell ggplot2 how to map variables to aesthetics, what graphical primitives to use, and it takes care of the details.
ggpmiscComprehensive R Archive Network (CRAN)ggpmisc 0.6.1Package ‘ggpmisc’ (Miscellaneous Extensions to ‘ggplot2’) is a set of extensions to R package ‘ggplot2’ (>= 3.0.0) with emphasis on annotations and plotting related to fitted models. Estimates from model fit objects can be displayed in ggplots as text, tables or equations. Predicted values, residuals, deviations and weights can be plotted for various model fit functions.
ggsciComprehensive R Archive Network (CRAN)ggsci 3.2.0ggsci offers a collection of ggplot2 color palettes inspired by scientific journals, data visualization libraries, science fiction movies, and TV shows.
openxlsxComprehensive R Archive Network (CRAN)openxlsx 4.2.7.1This R package simplifies the creation of .xlsx files by providing a high level interface to writing, styling and editing worksheets. Through the use of Rcpp, read/write times are comparable to the xlsx and XLConnect packages with the added benefit of removing the dependency on Java.
readxlComprehensive R Archive Network (CRAN)readxl 1.4.3The readxl package makes it easy to get data out of Excel and into R. Compared to many of the existing packages (e.g. gdata, xlsx, xlsReadWrite) readxl has no external dependencies, so it’s easy to install and use on all operating systems. It is designed to work with tabular data.
reshape2Comprehensive R Archive Network (CRAN)reshape2 1.4.4Reshape2 is a reboot of the reshape package. It's been over five years since the first release of reshape, and in that time I've learned a tremendous amount about R programming, and how to work with data in R. Reshape2 uses that knowledge to make a new package for reshaping data that is much more focused and much much faster.
stringrComprehensive R Archive Network (CRAN)stringr 1.5.1Strings are not glamorous, high-profile components of R, but they do play a big role in many data cleaning and preparation tasks. The stringr package provides a cohesive set of functions designed to make working with strings as easy as possible.
tidytextComprehensive R Archive Network (CRAN)tidytext 0.4.2Using tidy data principles can make many text mining tasks easier, more effective, and consistent with tools already in wide use. Much of the infrastructure needed for text mining with tidy data frames already exists in packages like dplyr, broom, tidyr, and ggplot2. In this package, we provide functions and supporting data sets to allow conversion of text to and from tidy formats, and to switch seamlessly between tidy tools and existing text mining packages. Check out our book to learn more about text mining using tidy data principles
tidyverseComprehensive R Archive Network (CRAN)tidyverse 2.0.0The tidyverse is an opinionated collection of R packages designed for data science. All packages share an underlying design philosophy, grammar, and data structures.
VennDiagramComprehensive R Archive Network (CRAN)VennDiagram 1.7.3VennDiagram is a R package for generating high-resolution, customizable Venn diagrams with up to four sets and Euler diagrams with up to three sets. Includes handling for several special cases including two-case scaling, and extensive customization of plot shape and structure.
VOSviewer Centre for Science and Technology Studies, Leiden University, The NetherlandsVOSviewer version 1.6.19VOSviewer is a software tool for constructing and visualizing bibliometric networks. These networks may for instance include journals, researchers, or individual publications, and they can be constructed based on citation, bibliographic coupling, co-citation, or co-authorship relations. VOSviewer also offers text mining functionality that can be used to construct and visualize co-occurrence networks of important terms extracted from a body of scientific literature.

References

  1. Kocarnik, J. M., et al. Cancer incidence, mortality, years of life lost, years lived with disability, and disability-adjusted life years for 29 cancer groups from 2010 to 2019: A systematic analysis for the global burden of disease study 2019. JAMA Oncol. 8 (3), 420-444 (2022).
  2. Soerjomataram, I., Bray, F. Planning for tomorrow: Global cancer incidence and the role of prevention 2020-2070. Nat Rev Clin Oncol. 18 (10), 663-672 (2021).
  3. Bray, F., et al. Global cancer statistics 2022: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 74 (3), 229-263 (2024).
  4. Zhang, Y., Zhang, Z. The history and advances in cancer immunotherapy: Understanding the characteristics of tumor-infiltrating immune cells and their therapeutic implications. Cell Mol Immunol. 17 (8), 807-821 (2020).
  5. Xu, X., et al. Metabolic reprogramming and epigenetic modifications in cancer: From the impacts and mechanisms to the treatment potential. Exp Mol Med. 55 (7), 1357-1370 (2023).
  6. Hanahan, D. Hallmarks of cancer: New dimensions. Cancer Discov. 12 (1), 31-46 (2022).
  7. Kashyap, A., et al. Quantification of tumor heterogeneity: From data acquisition to metric generation. Trends Biotechnol. 40 (6), 647-676 (2022).
  8. Vredevoogd, D. W., Peeper, D. S. Heterogeneity in functional genetic screens: Friend or foe. Front Immunol. 14, 1162706(2023).
  9. Jovic, D., et al. Single-cell RNA sequencing technologies and applications: A brief overview. Clin Transl Med. 12 (3), e694(2022).
  10. Hong, M., et al. RNA sequencing: New technologies and applications in cancer research. J Hematol Oncol. 13 (1), 166(2020).
  11. Moed, H. F. New developments in the use of citation analysis in research evaluation. Arch Immunol Ther Exp (Warsz). 57 (1), 13-18 (2009).
  12. Ninkov, A., Frank, J. R., Maggio, L. A. Bibliometrics: Methods for studying academic publishing. Perspect Med Educ. 11 (3), 173-176 (2022).
  13. Li, X., Wang, L., Wang, L., Feng, Z., Peng, C. Single-cell sequencing of hepatocellular carcinoma reveals cell interactions and cell heterogeneity in the microenvironment. Int J Gen Med. 14, 10141-10153 (2021).
  14. Li, Y., Jin, J., Bai, F. Cancer biology deciphered by single-cell transcriptomic sequencing. Protein Cell. 13 (3), 167-179 (2022).
  15. Bai, X., Li, Y., Zeng, X., Zhao, Q., Zhang, Z. Single-cell sequencing technology in tumor research. Clin Chim Acta. 518, 101-109 (2021).
  16. Zhang, L., et al. Worldwide research trends on tumor burden and immunotherapy: A bibliometric analysis. Int J Surg. 110 (3), 1699-1710 (2024).
  17. Ghorbani, B. D. A scientometrics research perspective in applied linguistics. Meihami, H., Esfandiari, R. , Springer Nature. Switzerland, Cham. 197-234 (2024).
  18. Chen, C. M., Leydesdorff, L. Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. J Assoc Inf Sci Technol. 65 (2), 334-351 (2014).
  19. Shen, S., et al. Analyzing and mapping the research status, hotspots, and frontiers of biological wound dressings: An in-depth data-driven assessment. Int J Pharm. 629, 122385(2022).
  20. Small, H., Sweeney, E., Greenlee, E. Clustering the science citation index using co-citations .2. Mapping science. Scientometrics. 8 (5-6), 321-340 (1985).
  21. Butler, A., Hoffman, P., Smibert, P., Papalexi, E., Satija, R. Integrating single-cell transcriptomic data across different conditions, technologies, and species. Nat Biotechnol. 36 (5), 411-420 (2018).
  22. Stuart, T., et al. Comprehensive integration of single-cell data. Cell. 177 (7), 1888-1902.e21 (2019).
  23. Baysoy, A., Bai, Z., Satija, R., Fan, R. The technological landscape and applications of single-cell multi-omics. Nat Rev Mol Cell Biol. 24 (10), 695-713 (2023).
  24. Macosko, E. Z., et al. Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets. Cell. 161 (5), 1202-1214 (2015).
  25. Tirosh, I., et al. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science (New York, N.Y.). 352 (6282), 189-196 (2016).
  26. Martinez-Simon, A., et al. Covid-19 publications in anaesthesiology journals: A bibliometric analysis. Br J Anaesth. 128 (3), e239-e241 (2022).
  27. Wen, L., Tang, F. Single-cell sequencing in stem cell biology. Genome Biol. 17, 71(2016).
  28. Zhang, Y., et al. Single-cell RNA sequencing in cancer research. J Exp Clin Cancer Res. 40 (1), 81(2021).
  29. Tang, F., et al. MRNA-seq whole-transcriptome analysis of a single-cell. Nat Methods. 6 (5), 377-382 (2009).
  30. Navin, N., et al. Tumour evolution inferred by single-cell sequencing. Nature. 472 (7341), 90-94 (2011).
  31. Tang, J., et al. Single-cell exome sequencing reveals multiple subclones in metastatic colorectal carcinoma. Genome Med. 13 (1), 148(2021).
  32. Song, H., et al. Single-cell analysis of human primary prostate cancer reveals the heterogeneity of tumor-associated epithelial cell states. Nat Commun. 13 (1), 141(2022).
  33. Zheng, X., et al. Single-cell transcriptomic profiling unravels the adenoma-initiation role of protein tyrosine kinases during colorectal tumorigenesis. Signal Transduct Target Ther. 7 (1), 60(2022).
  34. Chan, T. J., Zhang, X., Mak, M. Biophysical informatics reveals distinctive phenotypic signatures and functional diversity of single-cell lineages. Bioinformatics. 39 (1), btac833(2023).
  35. Chen, Y. P., et al. Single-cell transcriptomics reveals regulators underlying immune cell diversity and immune subtypes associated with prognosis in nasopharyngeal carcinoma. Cell Res. 30 (11), 1024-1042 (2020).
  36. Small, H. Co-citation in scientific literature - new measure of relationship between 2 documents. J Am Soc Inf Sci. 24 (4), 265-269 (1973).
  37. Villani, A. -C., et al. Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors. Science (New York, N.Y.). 356 (6335), eaah4573(2017).
  38. Sun, D., et al. Identifying phenotype-associated subpopulations by integrating bulk and single-cell sequencing data. Nat Biotechnol. 40 (4), 527-538 (2022).
  39. Pe'er, D., et al. Tumor heterogeneity. Cancer Cell. 39 (8), 1015-1017 (2021).
  40. Liu, X., et al. Single-cell transcriptomics links malignant T cells to the tumor immune landscape in cutaneous T-cell lymphoma. Nat Commun. 13 (1), 1158(2022).
  41. Yan, Y., et al. Clonal phylogeny and evolution of critical cytogenetic aberrations in multiple myeloma at single-cell level by qm-fish. Blood Adv. 6 (2), 441-451 (2022).

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Tags

Cancer ResearchBibliometric AnalysisImmune MicroenvironmentTumor EvolutionClonal EvolutionIntra Tumor HeterogeneityDrug Delivery SystemsCo Authorship AnalysisKeyword Co Occurrence

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