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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:

Single cells make big data: New challenges and opportunities in transcriptomics
Philipp Angerer, Lukas M. Simon, Sophie Tritschler, et al.
Current Opinion in Systems Biology (2017) Vol. 4, pp. 85-91
Open Access | Times Cited: 214

Showing 1-25 of 214 citing articles:

SCANPY: large-scale single-cell gene expression data analysis
F. Alexander Wolf, Philipp Angerer, Fabian J. Theis
Genome biology (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 5734
Current best practices in single‐cell RNA‐seq analysis: a tutorial
Malte D. Luecken, Fabian J. Theis
Molecular Systems Biology (2019) Vol. 15, Iss. 6
Open Access | Times Cited: 1672
A comparison of single-cell trajectory inference methods
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, et al.
Nature Biotechnology (2019) Vol. 37, Iss. 5, pp. 547-554
Open Access | Times Cited: 1324
Deep learning: new computational modelling techniques for genomics
Gökçen Eraslan, Žiga Avsec, Julien Gagneur, et al.
Nature Reviews Genetics (2019) Vol. 20, Iss. 7, pp. 389-403
Closed Access | Times Cited: 930
Single-cell RNA-seq denoising using a deep count autoencoder
Gökçen Eraslan, Lukas M. Simon, Maria Mircea, et al.
Nature Communications (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 843
scGen predicts single-cell perturbation responses
Mohammad Lotfollahi, F. Alexander Wolf, Fabian J. Theis
Nature Methods (2019) Vol. 16, Iss. 8, pp. 715-721
Closed Access | Times Cited: 416
A test metric for assessing single-cell RNA-seq batch correction
Maren Büttner, Zhichao Miao, F. Alexander Wolf, et al.
Nature Methods (2018) Vol. 16, Iss. 1, pp. 43-49
Open Access | Times Cited: 371
Probabilistic harmonization and annotation of single‐cell transcriptomics data with deep generative models
Chenling Xu, Romain Lopez, Edouard Mehlman, et al.
Molecular Systems Biology (2021) Vol. 17, Iss. 1
Open Access | Times Cited: 362
An integrated cell atlas of the lung in health and disease
Lisa Sikkema, Ciro Ramírez-Suástegui, Daniel Strobl, et al.
Nature Medicine (2023) Vol. 29, Iss. 6, pp. 1563-1577
Open Access | Times Cited: 310
Clustering single-cell RNA-seq data with a model-based deep learning approach
Tian Tian, Ji Wan, Qi Song, et al.
Nature Machine Intelligence (2019) Vol. 1, Iss. 4, pp. 191-198
Closed Access | Times Cited: 277
The Human Lung Cell Atlas: A High-Resolution Reference Map of the Human Lung in Health and Disease
Herbert B. Schiller, Daniel T. Montoro, Lukas M. Simon, et al.
American Journal of Respiratory Cell and Molecular Biology (2019) Vol. 61, Iss. 1, pp. 31-41
Open Access | Times Cited: 215
scGPT: toward building a foundation model for single-cell multi-omics using generative AI
Haotian Cui, Xiaoming Wang, Hassaan Maan, et al.
Nature Methods (2024) Vol. 21, Iss. 8, pp. 1470-1480
Open Access | Times Cited: 212
Concepts and limitations for learning developmental trajectories from single cell genomics
Sophie Tritschler, Maren Büttner, David S. Fischer, et al.
Development (2019) Vol. 146, Iss. 12
Open Access | Times Cited: 204
Accelerating Climate Resilient Plant Breeding by Applying Next-Generation Artificial Intelligence
Antoine Harfouche, Daniel Jacobson, David Kainer, et al.
Trends in biotechnology (2019) Vol. 37, Iss. 11, pp. 1217-1235
Open Access | Times Cited: 191
Deep Learning with Microfluidics for Biotechnology
Jason Riordon, Dušan Sovilj, Scott Sanner, et al.
Trends in biotechnology (2018) Vol. 37, Iss. 3, pp. 310-324
Closed Access | Times Cited: 186
Spatial Metabolomics and Imaging Mass Spectrometry in the Age of Artificial Intelligence
Theodore Alexandrov
Annual Review of Biomedical Data Science (2020) Vol. 3, Iss. 1, pp. 61-87
Open Access | Times Cited: 181
Applications of single-cell RNA sequencing in drug discovery and development
Bram Van de Sande, Joon Sang Lee, Euphemia Mutasa-Gottgens, et al.
Nature Reviews Drug Discovery (2023) Vol. 22, Iss. 6, pp. 496-520
Open Access | Times Cited: 165
Over 1000 tools reveal trends in the single-cell RNA-seq analysis landscape
Luke Zappia, Fabian J. Theis
Genome biology (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 112
An integrated cell atlas of the human lung in health and disease
Lisa Sikkema, Daniel Strobl, Luke Zappia, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2022)
Open Access | Times Cited: 80
A review of deep learning applications in human genomics using next-generation sequencing data
W. Alharbi, Mamoon Rashid
Human Genomics (2022) Vol. 16, Iss. 1
Open Access | Times Cited: 64
Cell lineage and communication network inference via optimization for single-cell transcriptomics
Shuxiong Wang, Matthew Karikomi, Adam L. MacLean, et al.
Nucleic Acids Research (2019) Vol. 47, Iss. 11, pp. e66-e66
Open Access | Times Cited: 135
Geometric Sketching Compactly Summarizes the Single-Cell Transcriptomic Landscape
Brian Hie, Hyunghoon Cho, Benjamin DeMeo, et al.
Cell Systems (2019) Vol. 8, Iss. 6, pp. 483-493.e7
Open Access | Times Cited: 132
CaSTLe – Classification of single cells by transfer learning: Harnessing the power of publicly available single cell RNA sequencing experiments to annotate new experiments
Yuval Lieberman, Lior Rokach, Tal Shay
PLoS ONE (2018) Vol. 13, Iss. 10, pp. e0205499-e0205499
Open Access | Times Cited: 109
A comparison of single-cell trajectory inference methods: towards more accurate and robust tools
Wouter Saelens, Robrecht Cannoodt, Helena Todorov, et al.
bioRxiv (Cold Spring Harbor Laboratory) (2018)
Open Access | Times Cited: 92
Double emulsion flow cytometry with high-throughput single droplet isolation and nucleic acid recovery
Kara K. Brower, Catherine Carswell-Crumpton, Sandy Klemm, et al.
Lab on a Chip (2020) Vol. 20, Iss. 12, pp. 2062-2074
Open Access | Times Cited: 84
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