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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A comparison of automatic cell identification methods for single-cell RNA sequencing data
Tamim Abdelaal, Lieke Michielsen, Davy Cats, et al.
Genome biology (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 491

Showing 1-25 of 491 citing articles:

Eleven grand challenges in single-cell data science
David Lähnemann, Johannes Köster, Ewa Szczurek, et al.
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 1037
A human cell atlas of fetal gene expression
Junyue Cao, Diana R. O’Day, Hannah A. Pliner, et al.
Science (2020) Vol. 370, Iss. 6518
Open Access | Times Cited: 619
Cross-tissue immune cell analysis reveals tissue-specific features in humans
Cecilia Domínguez Conde, Chuan Xu, Lorna B. Jarvis, et al.
Science (2022) Vol. 376, Iss. 6594
Open Access | Times Cited: 577
Best practices for single-cell analysis across modalities
Lukas Heumos, Anna C. Schaar, Christopher Lance, et al.
Nature Reviews Genetics (2023) Vol. 24, Iss. 8, pp. 550-572
Open Access | Times Cited: 513
Fully-automated and ultra-fast cell-type identification using specific marker combinations from single-cell transcriptomic data
Aleksandr Ianevski, Anil K Giri, Tero Aittokallio
Nature Communications (2022) Vol. 13, Iss. 1
Open Access | Times Cited: 411
Mapping single-cell data to reference atlases by transfer learning
Mohammad Lotfollahi, Mohsen Naghipourfar, Malte D. Luecken, et al.
Nature Biotechnology (2021) Vol. 40, Iss. 1, pp. 121-130
Open Access | Times Cited: 386
Massively parallel Cas13 screens reveal principles for guide RNA design
Hans‐Hermann Wessels, Alejandro Méndez‐Mancilla, Xinyi Guo, et al.
Nature Biotechnology (2020) Vol. 38, Iss. 6, pp. 722-727
Open Access | Times Cited: 333
Benchmarking of cell type deconvolution pipelines for transcriptomics data
Francisco Avila Cobos, José Alquicira-Hernández, Joseph E. Powell, et al.
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 325
scBERT as a large-scale pretrained deep language model for cell type annotation of single-cell RNA-seq data
Fan Yang, Wenchuan Wang, Fang Wang, et al.
Nature Machine Intelligence (2022) Vol. 4, Iss. 10, pp. 852-866
Open Access | Times Cited: 266
scCATCH: Automatic Annotation on Cell Types of Clusters from Single-Cell RNA Sequencing Data
Xin Shao, Jie Liao, Xiaoyan Lu, et al.
iScience (2020) Vol. 23, Iss. 3, pp. 100882-100882
Open Access | Times Cited: 256
Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data
Tallulah Andrews, Vladimir Yu Kiselev, Davis J. McCarthy, et al.
Nature Protocols (2020) Vol. 16, Iss. 1, pp. 1-9
Closed Access | Times Cited: 250
Atlas of clinically distinct cell states and ecosystems across human solid tumors
Bogdan Luca, Chloé B. Steen, Magdalena Matusiak, et al.
Cell (2021) Vol. 184, Iss. 21, pp. 5482-5496.e28
Open Access | Times Cited: 204
Single-Cell Transcriptome Analysis in Plants: Advances and Challenges
Rahul Shaw, Xin Tian, Jian Xu
Molecular Plant (2020) Vol. 14, Iss. 1, pp. 115-126
Open Access | Times Cited: 199
The single-cell eQTLGen consortium
Monique G.P. van der Wijst, DH de Vries, Hilde E. Groot, et al.
eLife (2020) Vol. 9
Open Access | Times Cited: 190
Automated methods for cell type annotation on scRNA-seq data
Giovanni Pasquini, Jesús Eduardo Rojo Arias, Patrick Schäfer, et al.
Computational and Structural Biotechnology Journal (2021) Vol. 19, pp. 961-969
Open Access | Times Cited: 183
A harmonized atlas of mouse spinal cord cell types and their spatial organization
D. Russ, Ryan B. Patterson Cross, Li Li, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 181
Advances in spatial transcriptomic data analysis
Ruben Dries, Jiaji Chen, Natalie Del Rossi, et al.
Genome Research (2021) Vol. 31, Iss. 10, pp. 1706-1718
Open Access | Times Cited: 172
Efficient and precise single-cell reference atlas mapping with Symphony
Joyce B. Kang, Aparna Nathan, Kathryn Weinand, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 164
Integrative analyses of single-cell transcriptome and regulome using MAESTRO
Chenfei Wang, Dongqing Sun, Xin Huang, et al.
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 159
Tutorial: guidelines for annotating single-cell transcriptomic maps using automated and manual methods
Zoe A. Clarke, Tallulah Andrews, Jawairia Atif, et al.
Nature Protocols (2021) Vol. 16, Iss. 6, pp. 2749-2764
Open Access | Times Cited: 159
Advances and Opportunities in Single-Cell Transcriptomics for Plant Research
Carolin Seyfferth, Jim Renema, Jos R. Wendrich, et al.
Annual Review of Plant Biology (2021) Vol. 72, Iss. 1, pp. 847-866
Open Access | Times Cited: 158
Single-cell analyses of aging, inflammation and senescence
Bora Uyar, Daniel H. Palmer, Axel Kowald, et al.
Ageing Research Reviews (2020) Vol. 64, pp. 101156-101156
Open Access | Times Cited: 138
Statistical and machine learning methods for spatially resolved transcriptomics data analysis
Zexian Zeng, Yawei Li, Yiming Li, et al.
Genome biology (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 125
Cell clustering for spatial transcriptomics data with graph neural networks
Jiachen Li, Siheng Chen, Xiaoyong Pan, et al.
Nature Computational Science (2022) Vol. 2, Iss. 6, pp. 399-408
Closed Access | Times Cited: 119
Machine learning meets omics: applications and perspectives
Rufeng Li, Lixin Li, Yungang Xu, et al.
Briefings in Bioinformatics (2021) Vol. 23, Iss. 1
Closed Access | Times Cited: 115
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