Aparna Nathan

3.9k total citations
27 papers, 832 citations indexed

About

Aparna Nathan is a scholar working on Molecular Biology, Immunology and Genetics. According to data from OpenAlex, Aparna Nathan has authored 27 papers receiving a total of 832 indexed citations (citations by other indexed papers that have themselves been cited), including 17 papers in Molecular Biology, 13 papers in Immunology and 7 papers in Genetics. Recurrent topics in Aparna Nathan's work include Single-cell and spatial transcriptomics (14 papers), T-cell and B-cell Immunology (11 papers) and Immune Cell Function and Interaction (7 papers). Aparna Nathan is often cited by papers focused on Single-cell and spatial transcriptomics (14 papers), T-cell and B-cell Immunology (11 papers) and Immune Cell Function and Interaction (7 papers). Aparna Nathan collaborates with scholars based in United States, United Kingdom and Japan. Aparna Nathan's co-authors include Soumya Raychaudhuri, Ilya Korsunsky, Jessica I. Beynor, Fan Zhang, D. Branch Moody, Elazer R. Edelman, Matthew A. Nugent, Laura T. Donlin, Joseph Mears and Kazuyoshi Ishigaki and has published in prestigious journals such as Nature, Proceedings of the National Academy of Sciences and Nature Communications.

In The Last Decade

Aparna Nathan

25 papers receiving 827 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Aparna Nathan United States 14 425 324 95 84 81 27 832
Anna Shcherbina Russia 18 253 0.6× 369 1.1× 196 2.1× 55 0.7× 102 1.3× 107 1.0k
Lies Boelen United Kingdom 14 235 0.6× 583 1.8× 33 0.3× 84 1.0× 132 1.6× 20 1.1k
Aurélien Corneau France 13 371 0.9× 206 0.6× 22 0.2× 64 0.8× 73 0.9× 27 646
Kyle J. Travaglini United States 6 615 1.4× 241 0.7× 41 0.4× 60 0.7× 112 1.4× 6 1.2k
Stephen Chang United States 6 642 1.5× 310 1.0× 71 0.7× 69 0.8× 122 1.5× 13 1.2k
Chuang Guo China 14 489 1.2× 387 1.2× 42 0.4× 32 0.4× 75 0.9× 30 915
Keith Shults United States 14 301 0.7× 294 0.9× 72 0.8× 35 0.4× 141 1.7× 23 991
Evgeny S. Egorov Russia 9 314 0.7× 444 1.4× 36 0.4× 59 0.7× 112 1.4× 10 726
Jovana Cupovic Switzerland 14 199 0.5× 602 1.9× 32 0.3× 38 0.5× 192 2.4× 15 887
Carl‐Magnus Högerkorp Sweden 11 264 0.6× 309 1.0× 45 0.5× 38 0.5× 105 1.3× 16 662

Countries citing papers authored by Aparna Nathan

Since Specialization
Citations

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

Fields of papers citing papers by Aparna Nathan

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Aparna Nathan. 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 Aparna Nathan. The network helps show where Aparna Nathan may publish in the future.

Co-authorship network of co-authors of Aparna Nathan

This figure shows the co-authorship network connecting the top 25 collaborators of Aparna Nathan. A scholar is included among the top collaborators of Aparna Nathan 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 Aparna Nathan. Aparna Nathan 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.
Nathan, Aparna, Stuart R. Lipsitz, Satoshi Koyama, et al.. (2025). Nonadherence to guidelines for genetic testing in families with ovarian cancer shows racial bias. Genetics in Medicine. 27(7). 101444–101444. 1 indexed citations
2.
Kotliar, Dylan, Michelle Curtis, Kathryn Weinand, et al.. (2025). Reproducible single-cell annotation of programs underlying T cell subsets, activation states and functions. Nature Methods. 22(9). 1964–1980.
3.
Valencia, Cristian, et al.. (2025). Modeling heterogeneity in single-cell perturbation states enhances detection of response eQTLs. Nature Genetics. 57(11). 2882–2890.
4.
Baglaenko, Yuriy, Zepeng Mu, Michelle Curtis, et al.. (2025). Precisely defining disease variant effects in CRISPR-edited single cells. Nature. 646(8083). 117–125. 1 indexed citations
5.
Tegtmeyer, Matthew, Jatin Arora, Samira Asgari, et al.. (2024). High-dimensional phenotyping to define the genetic basis of cellular morphology. Nature Communications. 15(1). 347–347. 21 indexed citations
6.
Nathan, Aparna, et al.. (2024). Estimating the sensitivity of genomic newborn screening for treatable inherited metabolic disorders. Genetics in Medicine. 27(1). 101284–101284. 3 indexed citations
7.
Lagattuta, Kaitlyn A., Ayano C. Kohlgruber, Nouran S. Abdelfattah, et al.. (2024). The T cell receptor sequence influences the likelihood of T cell memory formation. Cell Reports. 44(1). 115098–115098. 1 indexed citations
8.
Gupta, Anika, Kathryn Weinand, Aparna Nathan, et al.. (2023). Dynamic regulatory elements in single-cell multimodal data implicate key immune cell states enriched for autoimmune disease heritability. Nature Genetics. 55(12). 2200–2210. 10 indexed citations
9.
Cuomo, Anna, Aparna Nathan, Soumya Raychaudhuri, Daniel G. MacArthur, & Joseph E. Powell. (2023). Single-cell genomics meets human genetics. Nature Reviews Genetics. 24(8). 535–549. 48 indexed citations
10.
Xiao, Qian, Joseph Mears, Aparna Nathan, et al.. (2023). Immunosuppression causes dynamic changes in expression QTLs in psoriatic skin. Nature Communications. 14(1). 6268–6268. 8 indexed citations
11.
Nathan, Aparna, Samira Asgari, Kazuyoshi Ishigaki, et al.. (2022). Single-cell eQTL models reveal dynamic T cell state dependence of disease loci. Nature. 606(7912). 120–128. 88 indexed citations
12.
Lagattuta, Kaitlyn A., Joyce B. Kang, Aparna Nathan, et al.. (2022). Repertoire analyses reveal T cell antigen receptor sequence features that influence T cell fate. Nature Immunology. 23(3). 446–457. 41 indexed citations
13.
Nathan, Aparna, Jessica I. Beynor, Yuriy Baglaenko, et al.. (2021). Multimodally profiling memory T cells from a tuberculosis cohort identifies cell state associations with demographics, environment and disease. Nature Immunology. 22(6). 781–793. 56 indexed citations
14.
Millard, Nghia, Ilya Korsunsky, Kathryn Weinand, et al.. (2021). Maximizing Statistical Power to Detect Differentially Abundant Cell States With scPOST. SSRN Electronic Journal. 1 indexed citations
15.
Reshef, Yakir, Laurie Rumker, Joyce B. Kang, et al.. (2021). Co-varying neighborhood analysis identifies cell populations associated with phenotypes of interest from single-cell transcriptomics. Nature Biotechnology. 40(3). 355–363. 27 indexed citations
16.
Millard, Nghia, Ilya Korsunsky, Kathryn Weinand, et al.. (2021). Maximizing statistical power to detect differentially abundant cell states with scPOST. Cell Reports Methods. 1(8). 100120–100120. 3 indexed citations
17.
Kang, Joyce B., Aparna Nathan, Kathryn Weinand, et al.. (2021). Efficient and precise single-cell reference atlas mapping with Symphony. Nature Communications. 12(1). 5890–5890. 85 indexed citations
18.
Nathan, Aparna, Yuriy Baglaenko, Chamith Y. Fonseka, Jessica I. Beynor, & Soumya Raychaudhuri. (2019). Multimodal single-cell approaches shed light on T cell heterogeneity. Current Opinion in Immunology. 61. 17–25. 11 indexed citations
19.
Gutiérrez‐Arcelus, María, Nikola C. Teslovich, Rafael B. Polidoro, et al.. (2019). Lymphocyte innateness defined by transcriptional states reflects a balance between proliferation and effector functions. Nature Communications. 10(1). 687–687. 102 indexed citations
20.
Gurman, Pablo, et al.. (2015). Recombinant tissue plasminogen activators (rtPA): A review. Clinical Pharmacology & Therapeutics. 97(3). 274–285. 45 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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