Megan Crow

4.5k total citations
21 papers, 1.2k citations indexed

About

Megan Crow is a scholar working on Molecular Biology, Neurology and Physiology. According to data from OpenAlex, Megan Crow has authored 21 papers receiving a total of 1.2k indexed citations (citations by other indexed papers that have themselves been cited), including 18 papers in Molecular Biology, 7 papers in Neurology and 3 papers in Physiology. Recurrent topics in Megan Crow's work include Single-cell and spatial transcriptomics (13 papers), Neuroinflammation and Neurodegeneration Mechanisms (7 papers) and Bioinformatics and Genomic Networks (4 papers). Megan Crow is often cited by papers focused on Single-cell and spatial transcriptomics (13 papers), Neuroinflammation and Neurodegeneration Mechanisms (7 papers) and Bioinformatics and Genomic Networks (4 papers). Megan Crow collaborates with scholars based in United States, United Kingdom and Canada. Megan Crow's co-authors include Jesse Gillis, Anirban Paul, Sara Ballouz, Franziska Denk, Stephen B. McMahon, Ricardo Raudales, Z. Josh Huang, Miao He, Zhi Huang and Douglas M. Lopes and has published in prestigious journals such as Cell, Proceedings of the National Academy of Sciences and Nucleic Acids Research.

In The Last Decade

Megan Crow

21 papers receiving 1.2k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Megan Crow United States 16 695 335 285 178 135 21 1.2k
Elizabeth Bien United States 5 662 1.0× 327 1.0× 390 1.4× 344 1.9× 215 1.6× 7 1.4k
Marco B. Rust Germany 23 899 1.3× 586 1.7× 252 0.9× 128 0.7× 114 0.8× 49 1.6k
Denise M. O. Ramirez United States 19 984 1.4× 640 1.9× 242 0.8× 133 0.7× 99 0.7× 31 1.8k
Stylianos Kosmidis United States 18 589 0.8× 264 0.8× 253 0.9× 98 0.6× 84 0.6× 23 1.2k
O. A. Kharchenko Russia 8 681 1.0× 529 1.6× 611 2.1× 110 0.6× 80 0.6× 12 1.6k
Elmer Guzman United States 12 665 1.0× 254 0.8× 401 1.4× 219 1.2× 79 0.6× 14 1.2k
Yoichiro Abe Japan 23 784 1.1× 422 1.3× 449 1.6× 173 1.0× 159 1.2× 69 1.8k
Guojun Ma China 8 588 0.8× 284 0.8× 270 0.9× 174 1.0× 67 0.5× 16 1.1k
Marcin Piechota Poland 22 550 0.8× 445 1.3× 185 0.6× 221 1.2× 83 0.6× 61 1.4k
Emelie Braun Sweden 5 951 1.4× 353 1.1× 283 1.0× 401 2.3× 110 0.8× 5 1.7k

Countries citing papers authored by Megan Crow

Since Specialization
Citations

This map shows the geographic impact of Megan Crow's research. 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 Megan Crow with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Megan Crow more than expected).

Fields of papers citing papers by Megan Crow

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Megan Crow. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Megan Crow. The network helps show where Megan Crow may publish in the future.

Co-authorship network of co-authors of Megan Crow

This figure shows the co-authorship network connecting the top 25 collaborators of Megan Crow. A scholar is included among the top collaborators of Megan Crow based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Megan Crow. Megan Crow is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Khan, Zia, Min Jung, Megan Crow, et al.. (2023). Whole genome sequencing across clinical trials identifies rare coding variants in GPR68 associated with chemotherapy-induced peripheral neuropathy. Genome Medicine. 15(1). 45–45. 6 indexed citations
2.
Suresh, Hamsini, Megan Crow, Nikolas L. Jorstad, et al.. (2023). Comparative single-cell transcriptomic analysis of primate brains highlights human-specific regulatory evolution. Nature Ecology & Evolution. 7(11). 1930–1943. 9 indexed citations
3.
Ballouz, Sara, Risa Karakida Kawaguchi, Maria T. Peña, et al.. (2023). The transcriptional legacy of developmental stochasticity. Nature Communications. 14(1). 7226–7226. 4 indexed citations
4.
Crow, Megan, Hamsini Suresh, J. Jack Lee, & Jesse Gillis. (2022). Coexpression reveals conserved gene programs that co-vary with cell type across kingdoms. Nucleic Acids Research. 50(8). 4302–4314. 19 indexed citations
5.
Harris, Benjamin, Megan Crow, Stephan Fischer, & Jesse Gillis. (2021). Single-cell co-expression analysis reveals that transcriptional modules are shared across cell types in the brain. Cell Systems. 12(7). 748–756.e3. 17 indexed citations
6.
Fischer, Stephan, Megan Crow, Benjamin Harris, & Jesse Gillis. (2021). Scaling up reproducible research for single-cell transcriptomics using MetaNeighbor. Nature Protocols. 16(8). 4031–4067. 18 indexed citations
7.
Lee, J. Jack, et al.. (2020). CoCoCoNet: conserved and comparative co-expression across a diverse set of species. Nucleic Acids Research. 48(W1). W566–W571. 28 indexed citations
8.
Crow, Megan, Nathaniel C. Lim, Sara Ballouz, Paul Pavlidis, & Jesse Gillis. (2019). Predictability of human differential gene expression. Proceedings of the National Academy of Sciences. 116(13). 6491–6500. 76 indexed citations
9.
Crow, Megan & Jesse Gillis. (2019). Single cell RNA-sequencing: replicability of cell types. Current Opinion in Neurobiology. 56. 69–77. 10 indexed citations
10.
Crow, Megan & Franziska Denk. (2019). RNA-seq data in pain research–an illustrated guide. Pain. 160(7). 1502–1504. 3 indexed citations
11.
Crow, Megan, Anirban Paul, Sara Ballouz, Zhi Huang, & Jesse Gillis. (2018). Characterizing the replicability of cell types defined by single cell RNA-sequencing data using MetaNeighbor. Nature Communications. 9(1). 884–884. 165 indexed citations
12.
Crow, Megan & Jesse Gillis. (2018). Co-expression in Single-Cell Analysis: Saving Grace or Original Sin?. Trends in Genetics. 34(11). 823–831. 29 indexed citations
13.
Kalish, Brian T., Lucas Cheadle, Siniša Hrvatin, et al.. (2018). Single-cell transcriptomics of the developing lateral geniculate nucleus reveals insights into circuit assembly and refinement. Proceedings of the National Academy of Sciences. 115(5). E1051–E1060. 54 indexed citations
14.
Paul, Anirban, Megan Crow, Ricardo Raudales, et al.. (2017). Transcriptional Architecture of Synaptic Communication Delineates GABAergic Neuron Identity. Cell. 171(3). 522–539.e20. 268 indexed citations
15.
Denk, Franziska, Megan Crow, Athanasios Didangelos, Douglas M. Lopes, & Stephen B. McMahon. (2016). Persistent Alterations in Microglial Enhancers in a Model of Chronic Pain. Cell Reports. 15(8). 1771–1781. 105 indexed citations
16.
Crow, Megan, Anirban Paul, Sara Ballouz, Zheng Huang, & Jesse Gillis. (2016). Exploiting single-cell expression to characterize co-expression replicability. Genome biology. 17(1). 101–101. 47 indexed citations
17.
Crow, Megan, Simone Sharma, Andrew Grant, et al.. (2015). HDAC4 is required for inflammation-associated thermal hypersensitivity. The FASEB Journal. 29(8). 3370–3378. 30 indexed citations
18.
Thakur, Matthew, Megan Crow, Emma Levine, et al.. (2014). Defining the nociceptor transcriptome. Frontiers in Molecular Neuroscience. 7. 87–87. 92 indexed citations
19.
Crow, Megan, Franziska Denk, & Stephen B. McMahon. (2013). Genes and epigenetic processes as prospective pain targets. Genome Medicine. 5(2). 12–12. 52 indexed citations
20.
Denk, Franziska, Wenlong Huang, Ben S. Sidders, et al.. (2013). HDAC inhibitors attenuate the development of hypersensitivity in models of neuropathic pain. Pain. 154(9). 1668–1679. 133 indexed citations

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.

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