Accounting for technical noise in single-cell RNA-seq experiments
Impact in
- Cancer Research 159
Classified as
- Journal
- Nature Methods
In The Last Decade
doi.org/10.1038/nmeth.2645 →Countries where authors are citing Accounting for technical noise in single-cell RNA-seq experiments
This map shows the geographic impact of Accounting for technical noise in single-cell RNA-seq experiments. 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 Accounting for technical noise in single-cell RNA-seq experiments with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Accounting for technical noise in single-cell RNA-seq experiments more than expected).
Fields of papers citing Accounting for technical noise in single-cell RNA-seq experiments
This network shows the impact of Accounting for technical noise in single-cell RNA-seq experiments. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Accounting for technical noise in single-cell RNA-seq experiments.
About Accounting for technical noise in single-cell RNA-seq experiments
This paper, published in 2013, received 684 indexed citations . Written by Philip Brennecke, Simon Anders, Jong Kim, Aleksandra A. Kolodziejczyk, Xiuwei Zhang, Valentina Proserpio, Bianka Baying, Vladimı́r Beneš, Sarah A. Teichmann and John C. Marioni covering the research area of Molecular Biology. It is primarily cited by scholars working on Molecular Biology (607 citations), Cancer Research (159 citations), Immunology (93 citations), Biophysics (82 citations) and Genetics (33 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.2645.