Most cited papers in Single-cell and spatial transcriptomics
What are the most cited papers in Single-cell and spatial transcriptomics?
The most cited paper in Single-cell and spatial transcriptomics is "edgeR : a Bioconductor package for differential expression analysis of digital gene expression data" (Mark D. Robinson, Davis James McCarthy, Gordon K. Smyth, 2009), with 45,449 citations. Next come "Comprehensive Integration of Single-Cell Data" (17,471) and "GSVA: gene set variation analysis for microarray and RNA-Seq data" (17,089).
Showing 50 of 100 papers. Every paper with more than 2,076 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | edgeR : a Bioconductor package for differential expression analysis of digital gene expression data Mark D. Robinson, Davis James McCarthy, Gordon K. Smyth · Bioinformatics | 11 Nov 2009 | 45,449 |
| 2 | Comprehensive Integration of Single-Cell Data Tim Stuart, Andrew W. Butler, Paul Hoffman et al. · Cell | Jun 2019 | 17,471 |
| 3 | GSVA: gene set variation analysis for microarray and RNA-Seq data Sonja Hänzelmann, Robert Castelo, Justin Guinney · BMC Bioinformatics | 16 Jan 2013 | 17,089 |
| 4 | Integrative genomics viewer James Theodore Robinson, Helga Thorvaldsdóttir, Wendy Winckler et al. · Nature Biotechnology | 2011 | 16,977 |
| 5 | Integrated analysis of multimodal single-cell data Yuhan Hao et al. · Cell | 31 May 2021 | 16,879 |
| 6 | Integrating single-cell transcriptomic data across different conditions, technologies, and species Andrew W. Butler, Paul Hoffman, Peter Smibert et al. · Nature Biotechnology | 2 Apr 2018 | 15,109 |
| 7 | Robust enumeration of cell subsets from tissue expression profiles Aaron M. Newman, Chih Long Liu, Michael Richard Green et al. · Nature Methods | 30 Mar 2015 | 14,493 |
| 8 | Bioconductor: open software development for computational biology and bioinformatics Robert C. Gentleman et al. · Genome biology | 15 Sep 2004 | 12,632 |
| 9 | Fast, sensitive and accurate integration of single-cell data with Harmony Ilya Korsunsky, Nghia Millard, Jean Fan et al. · Nature Methods | 18 Nov 2019 | 11,339 |
| 10 | The UK Biobank resource with deep phenotyping and genomic data Clare Bycroft, Colin Freeman, Desislava Petkova et al. · Nature | 2 Oct 2018 | 10,121 |
| 11 | SCANPY: large-scale single-cell gene expression data analysis F. Alexander Wolf, Philipp Angerer, Fabian Joachim Theis · Genome biology | 6 Feb 2018 | 9,901 |
| 12 | deepTools2: a next generation web server for deep-sequencing data analysis Fidel Ramírez, Devon Patrick Ryan, Björn Andreas Grüning et al. · Nucleic Acids Research | 13 Apr 2016 | 9,381 |
| 13 | A comparison of normalization methods for high density oligonucleotide array data based on variance and bias Benjamin M. Bolstad, Rafael A. Irizarry, Magnus Åstrand et al. · Bioinformatics | 21 Jan 2003 | 8,511 |
| 14 | The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells Cole Trapnell, Davide Cacchiarelli, Jonna Grimsby et al. · Nature Biotechnology | 23 Mar 2014 | 8,179 |
| 15 | Massively parallel digital transcriptional profiling of single cells Grace X. Y. Zheng et al. · Nature Communications | 16 Jan 2017 | 8,144 |
| 16 | Spatial reconstruction of single-cell gene expression data Rahul Satija, Jeffrey A. Farrell, David G. Gennert et al. · Nature Biotechnology | 13 Apr 2015 | 8,018 |
| 17 | SCENIC: single-cell regulatory network inference and clustering Sara Aibar, Carmen Bravo González‐Blas, Thomas Moerman et al. · Nature Methods | 9 Oct 2017 | 7,321 |
| 18 | Genome-wide atlas of gene expression in the adult mouse brain Ed S. Lein et al. · Nature | 6 Dec 2006 | 6,052 |
| 19 | An Integrated Genomic Analysis of Human Glioblastoma Multiforme Donald Williams Parsons et al. · Science | 4 Sep 2008 | 5,914 |
| 20 | Dimensionality reduction for visualizing single-cell data using UMAP Étienne Becht, Leland McInnes, John Healy et al. · Nature Biotechnology | 3 Dec 2018 | 5,903 |
| 21 | Reversed graph embedding resolves complex single-cell trajectories Xiaojie Qiu, Qi Rong Mao, Ying Tang et al. · Nature Methods | 21 Aug 2017 | 5,638 |
| 22 | GEPIA2: an enhanced web server for large-scale expression profiling and interactive analysis Zefang Tang, Boxi Kang, Chenwei Li et al. · Nucleic Acids Research | 10 May 2019 | 5,608 |
| 23 | Dictionary learning for integrative, multimodal and scalable single-cell analysis Yuhan Hao, Tim Stuart, Madeline H. Kowalski et al. · Nature Biotechnology | 25 May 2023 | 5,523 |
| 24 | Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq Itay Tirosh et al. · Science | 7 Apr 2016 | 5,440 |
| 25 | Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression Christoph Hafemeister, Rahul Satija · Genome biology | Dec 2019 | 5,112 |
| 26 | Determining cell type abundance and expression from bulk tissues with digital cytometry Aaron M. Newman, Chloé Beate Steen, Chih Long Liu et al. · Nature Biotechnology | 6 May 2019 | 5,050 |
| 27 | Summaries of Affymetrix GeneChip probe level data Rafael A. Irizarry · Nucleic Acids Research | 11 Feb 2003 | 4,994 |
| 28 | The single-cell transcriptional landscape of mammalian organogenesis Junyue Cao, Malte Spielmann, Xiaojie Qiu et al. · Nature | 20 Feb 2019 | 4,970 |
| 29 | DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors Christopher S. McGinnis, Lyndsay M. Murrow, Zev J. Gartner · Cell Systems | Apr 2019 | 4,865 |
| 30 | xCell: digitally portraying the tissue cellular heterogeneity landscape Dvir Aran, Zicheng Hu, Atul Janardhan Butte · Genome biology | 15 Nov 2017 | 4,825 |
| 31 | RNA velocity of single cells Gioele La Manno et al. · Nature | 7 Aug 2018 | 4,678 |
| 32 | Cellpose: a generalist algorithm for cellular segmentation Carsen Stringer, Tim Wang, Michalis Michaelos et al. · Nature Methods | 14 Dec 2020 | 4,662 |
| 33 | Full-length RNA-seq from single cells using Smart-seq2 Simone Picelli, Omid R. Faridani, Åsa K. Björklund et al. · Nature Protocols | 2014 | 4,639 |
| 34 | Profiling Tumor Infiltrating Immune Cells with CIBERSORT Binbin Chen, Michael Siavash Khodadoust, Chih Long Liu et al. · Methods in molecular biology | 2018 | 4,265 |
| 35 | Senescence-Associated Secretory Phenotypes Reveal Cell-Nonautonomous Functions of Oncogenic RAS and the p53 Tumor Suppressor Jean‐Philippe Coppé · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) | 13 Jan 2009 | 4,254 |
| 36 | mRNA-Seq whole-transcriptome analysis of a single cell Fuchou Tang, Cátálin Bárbácioru, Yangzhou Wang et al. · Nature Methods | 6 Apr 2009 | 4,191 |
| 37 | Visualization and analysis of gene expression in tissue sections by spatial transcriptomics Patrik L. Ståhl et al. · Science | 30 Jun 2016 | 4,138 |
| 38 | Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression Étienne Becht, Nicolás A. Giraldo, Laetitia Lacroix et al. · Genome biology | 20 Oct 2016 | 4,124 |
| 39 | CellPhoneDB: inferring cell–cell communication from combined expression of multi-subunit ligand–receptor complexes Mirjana Efremova, Miquel Vento-Tormo, Sarah Amalia Teichmann et al. · Nature Protocols | 26 Feb 2020 | 3,773 |
| 40 | ilastik: interactive machine learning for (bio)image analysis Stuart Berg, Dominik Kutra, Thorben Kroeger et al. · Nature Methods | 30 Sep 2019 | 3,760 |
| 41 | Droplet Barcoding for Single-Cell Transcriptomics Applied to Embryonic Stem Cells Allon Moshe Klein, Linas Mažutis, Ilke Akartuna et al. · Cell | May 2015 | 3,759 |
| 42 | MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data Greg Finak, Andrew McDavid, Masanao Yajima et al. · Genome biology | Dec 2015 | 3,705 |
| 43 | Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics Kelly Street, Davide Risso, Russell B. Fletcher et al. · BMC Genomics | 18 Jun 2018 | 3,551 |
| 44 | Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq Amit Zeisel, Ana B. Muñoz‐Manchado, Simone Codeluppi et al. · Science | 21 Feb 2015 | 3,520 |
| 45 | Normalization for cDNA microarray data: a robust composite method addressing single and multiple slide systematic variation Jean Yee Hwa Yang · Nucleic Acids Research | 15 Feb 2002 | 3,514 |
| 46 | DNA methylation arrays as surrogate measures of cell mixture distribution Eugene Andres Houseman, William P. Accomando, Devin C. Koestler et al. · BMC Bioinformatics | 8 May 2012 | 3,505 |
| 47 | Simultaneous epitope and transcriptome measurement in single cells Marlon Stoeckius, Christoph Hafemeister, William Stephenson et al. · Nature Methods | 31 Jul 2017 | 3,429 |
| 48 | An anatomically comprehensive atlas of the adult human brain transcriptome Michael Hawrylycz et al. · Nature | Sep 2012 | 3,404 |
| 49 | The prognostic landscape of genes and infiltrating immune cells across human cancers Andrew J. Gentles, Aaron M. Newman, Chih Long Liu et al. · Nature Medicine | 20 Jul 2015 | 3,329 |
| 50 | IDBA-UD: a de novo assembler for single-cell and metagenomic sequencing data with highly uneven depth Yu Peng, Henry C. M. Leung, Siu‐Ming Yiu et al. · Bioinformatics | 11 Apr 2012 | 3,265 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.