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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:

Splatter: simulation of single-cell RNA sequencing data
Luke Zappia, Belinda Phipson, Alicia Oshlack
Genome biology (2017) Vol. 18, Iss. 1
Open Access | Times Cited: 748

Showing 1-25 of 748 citing articles:

DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors
Christopher S. McGinnis, Lyndsay M. Murrow, Zev J. Gartner
Cell Systems (2019) Vol. 8, Iss. 4, pp. 329-337.e4
Open Access | Times Cited: 2543
Slingshot: cell lineage and pseudotime inference for single-cell transcriptomics
Kelly Street, Davide Risso, Russell B. Fletcher, et al.
BMC Genomics (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 2169
Scrublet: Computational Identification of Cell Doublets in Single-Cell Transcriptomic Data
Samuel L. Wolock, Romain Lopez, Allon M. Klein
Cell Systems (2019) Vol. 8, Iss. 4, pp. 281-291.e9
Open Access | Times Cited: 1744
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
Heavy-tailed prior distributions for sequence count data: removing the noise and preserving large differences
Anqi Zhu, Joseph G. Ibrahim, Michael I. Love
Bioinformatics (2018) Vol. 35, Iss. 12, pp. 2084-2092
Open Access | Times Cited: 1455
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
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: 992
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
A benchmark of batch-effect correction methods for single-cell RNA sequencing data
Hoa Tran, Kok Siong Ang, Marion Chevrier, et al.
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 828
Visualizing structure and transitions in high-dimensional biological data
Kevin R. Moon, David van Dijk, Zheng Wang, et al.
Nature Biotechnology (2019) Vol. 37, Iss. 12, pp. 1482-1492
Open Access | Times Cited: 798
Efficient integration of heterogeneous single-cell transcriptomes using Scanorama
Brian Hie, Bryan D. Bryson, Bonnie Berger
Nature Biotechnology (2019) Vol. 37, Iss. 6, pp. 685-691
Open Access | Times Cited: 738
Benchmarking atlas-level data integration in single-cell genomics
Malte D. Luecken, Maren Büttner, Kridsadakorn Chaichoompu, et al.
Nature Methods (2021) Vol. 19, Iss. 1, pp. 41-50
Open Access | Times Cited: 727
BBKNN: fast batch alignment of single cell transcriptomes
Krzysztof Polański, Matthew D. Young, Zhichao Miao, et al.
Bioinformatics (2019) Vol. 36, Iss. 3, pp. 964-965
Open Access | Times Cited: 708
Orchestrating single-cell analysis with Bioconductor
Robert A. Amezquita, Aaron T. L. Lun, Étienne Becht, et al.
Nature Methods (2019) Vol. 17, Iss. 2, pp. 137-145
Open Access | Times Cited: 697
scMC learns biological variation through the alignment of multiple single-cell genomics datasets
Lihua Zhang, Qing Nie
Genome biology (2021) Vol. 22, Iss. 1
Open Access | Times Cited: 643
Confronting false discoveries in single-cell differential expression
Jordan W. Squair, Matthieu Gautier, Claudia Kathe, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 567
Immune Landscape of Viral- and Carcinogen-Driven Head and Neck Cancer
Anthony R. Cillo, Cornelius Kürten, Tracy Tabib, et al.
Immunity (2020) Vol. 52, Iss. 1, pp. 183-199.e9
Open Access | Times Cited: 516
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Ruochen Jiang, Tianyi Sun, Dongyuan Song, et al.
Genome biology (2022) Vol. 23, Iss. 1
Open Access | Times Cited: 425
Identifying gene expression programs of cell-type identity and cellular activity with single-cell RNA-Seq
Dylan Kotliar, Adrian Veres, M. Aurel Nagy, et al.
eLife (2019) Vol. 8
Open Access | Times Cited: 374
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
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: 358
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
Angelo Duò, Mark D. Robinson, Charlotte Soneson
F1000Research (2018) Vol. 7, pp. 1141-1141
Open Access | Times Cited: 306
Probabilistic cell-type assignment of single-cell RNA-seq for tumor microenvironment profiling
Allen W. Zhang, Ciara H. O’Flanagan, Elizabeth A. Chavez, et al.
Nature Methods (2019) Vol. 16, Iss. 10, pp. 1007-1015
Open Access | Times Cited: 306
STARsolo: accurate, fast and versatile mapping/quantification of single-cell and single-nucleus RNA-seq data
Benjamin Kaminow, Dinar Yunusov, Alexander Dobin
bioRxiv (Cold Spring Harbor Laboratory) (2021)
Open Access | Times Cited: 283
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
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