SC3: consensus clustering of single-cell RNA-seq data
Impact in
- Cancer Research 195
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth.4236 →Countries where authors are citing SC3: consensus clustering of single-cell RNA-seq data
This map shows the geographic impact of SC3: consensus clustering of single-cell RNA-seq data. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by SC3: consensus clustering of single-cell RNA-seq data with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites SC3: consensus clustering of single-cell RNA-seq data more than expected).
Fields of papers citing SC3: consensus clustering of single-cell RNA-seq data
This network shows the impact of SC3: consensus clustering of single-cell RNA-seq data. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the SC3: consensus clustering of single-cell RNA-seq data.
About SC3: consensus clustering of single-cell RNA-seq data
This paper, published in 2017, received 941 indexed citations . Written by Vladimir Yu Kiselev, Kristina Kirschner, Michael T. Schaub, Tallulah Andrews, Andrew Yiu, Tamir Chandra, Kedar Nath Natarajan, Wolf Reik, Mauricio Barahona and Anthony R. Green covering the research area of Molecular Biology and Cancer Research. It is primarily cited by scholars working on Molecular Biology (831 citations), Cancer Research (195 citations), Biophysics (135 citations), Immunology (103 citations) and Artificial Intelligence (60 citations). Published in Nature Methods.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.
This paper is also available at doi.org/10.1038/nmeth.4236.