Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review ...Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately.展开更多
Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet...Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.展开更多
文摘Purpose： We present a systematic review of the literature concerning major aspects of science mapping to serve two primary purposes： First, to demonstrate the use of a science mapping approach to perform the review so that researchers may apply the procedure to the review of a scientific domain of their own interest, and second, to identify major areas of research activities concerning science mapping, intellectual milestones in the development of key specialties, evolutionary stages of major specialties involved, and the dynamics of transitions from one specialty to another.Design/methodology/approach： We first introduce a theoretical framework of the evolution of a scientific specialty. Then we demonstrate a generic search strategy that can be used to construct a representative dataset of bibliographic records of a domain of research. Next, progressively synthesized co-citation networks are constructed and visualized to aid visual analytic studies of the domain＇s structural and dynamic patterns and trends. Finally, trajectories of citations made by particular types of authors and articles are presented to illustrate the predictive potential of the analytic approach.Findings： The evolution of the science mapping research involves the development of a number of interrelated specialties. Four major specialties are discussed in detail in terms of four evolutionary stages： conceptualization, tool construction, application, and codification. Underlying connections between major specialties are also explored. The predictive analysis demonstrates citations trajectories of potentially transformative contributions.Research limitations： The systematic review is primarily guided by citation patterns in the dataset retrieved from the literature. The scope of the data is limited by the source of the retrieval, i.e. the Web of Science, and the composite query used. An iterative query refinement is possible if one would like to improve the data quality, although the current approach serves our purpose adequately.
基金This work was supported by a startup fund from Southern Medical University.
文摘Functional enrichment analysis is pivotal for interpreting highthroughput omics data in life science.It is crucial for this type of tool to use the latest annotation databases for as many organisms as possible.To meet these requirements,we present here an updated version of our popular Bioconductor package,clusterProfiler 4.0.This package has been enhanced considerably compared with its original version published 9 years ago.The new version provides a universal interface for functional enrichment analysis in thousands of organisms based on internally supported ontologies and pathways as well as annotation data provided by users or derived from online databases.It also extends the dplyr and ggplot2 packages to offer tidy interfaces for data operation and visualization.Other new features include gene set enrichment analysis and comparison of enrichment results from multiple gene lists.We anticipate that clusterProfiler 4.0 will be applied to a wide range of scenarios across diverse organisms.
