Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

An accurate and robust imputation method scImpute for single-cell RNA-seq data
Wei Vivian Li, Jingyi Jessica Li
Nature Communications (2018) Vol. 9, Iss. 1
Open Access | Times Cited: 633

Showing 1-25 of 633 citing articles:

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: 1658
Resolving the fibrotic niche of human liver cirrhosis at single-cell level
Prakash Ramachandran, Ross Dobie, John R. Wilson‐Kanamori, et al.
Nature (2019) Vol. 575, Iss. 7783, pp. 512-518
Open Access | Times Cited: 1193
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: 990
Challenges in unsupervised clustering of single-cell RNA-seq data
Vladimir Yu Kiselev, Tallulah Andrews, Martin Hemberg
Nature Reviews Genetics (2019) Vol. 20, Iss. 5, pp. 273-282
Closed Access | Times Cited: 985
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
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis
Geng Chen, Baitang Ning, Tieliu Shi
Frontiers in Genetics (2019) Vol. 10
Open Access | Times Cited: 792
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: 746
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
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
SAVER: gene expression recovery for single-cell RNA sequencing
Mo Huang, Jingshu Wang, Eduardo A. Torre, et al.
Nature Methods (2018) Vol. 15, Iss. 7, pp. 539-542
Open Access | Times Cited: 676
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: 424
Metabolic landscape of the tumor microenvironment at single cell resolution
Zhengtao Xiao, Ziwei Dai, Jason W. Locasale
Nature Communications (2019) Vol. 10, Iss. 1
Open Access | Times Cited: 381
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
Single-cell RNA sequencing identifies celltype-specific cis-eQTLs and co-expression QTLs
Monique G.P. van der Wijst, Harm Brugge, Dylan H. de Vries, et al.
Nature Genetics (2018) Vol. 50, Iss. 4, pp. 493-497
Open Access | Times Cited: 342
MetaCell: analysis of single-cell RNA-seq data using K-nn graph partitions
Yael Baran, Akhiad Bercovich, Arnau Sebé-Pedrós, et al.
Genome biology (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 300
Droplet scRNA-seq is not zero-inflated
Valentine Svensson
Nature Biotechnology (2020) Vol. 38, Iss. 2, pp. 147-150
Open Access | Times Cited: 300
DrImpute: imputing dropout events in single cell RNA sequencing data
Wuming Gong, Il‐Youp Kwak, Pruthvi Pota, et al.
BMC Bioinformatics (2018) Vol. 19, Iss. 1
Open Access | Times Cited: 291
Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities
Marinka Żitnik, Francis Nguyen, Bo Wang, et al.
Information Fusion (2018) Vol. 50, pp. 71-91
Open Access | Times Cited: 286
Exploring single-cell data with deep multitasking neural networks
Matthew Amodio, David van Dijk, Krishnan Srinivasan, et al.
Nature Methods (2019) Vol. 16, Iss. 11, pp. 1139-1145
Open Access | Times Cited: 284
Embracing the dropouts in single-cell RNA-seq analysis
Peng Qiu
Nature Communications (2020) Vol. 11, Iss. 1
Open Access | Times Cited: 276
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: 275
scGNN is a novel graph neural network framework for single-cell RNA-Seq analyses
Juexin Wang, Anjun Ma, Yuzhou Chang, et al.
Nature Communications (2021) Vol. 12, Iss. 1
Open Access | Times Cited: 270
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: 238
A systematic evaluation of single-cell RNA-sequencing imputation methods
Wenpin Hou, Zhicheng Ji, Hongkai Ji, et al.
Genome biology (2020) Vol. 21, Iss. 1
Open Access | Times Cited: 232
DeepImpute: an accurate, fast, and scalable deep neural network method to impute single-cell RNA-seq data
Cédric Arisdakessian, Olivier Poirion, Breck Yunits, et al.
Genome biology (2019) Vol. 20, Iss. 1
Open Access | Times Cited: 231
Page 1 - Next Page

Scroll to top