Cited by 198Single-cell and spatial transcriptomicsGene expression and cancer classificationGenomics and Phylogenetic Studies

The DOI matches its Crossref record; 36 of the 38 references it cites were found in a registry or an index, and 1 are not checked yet.

False signals induced by single-cell imputation

Open access

Andrews, Tallulah S., Hemberg, Martin·2018·F1000Research

DOI 10.12688/f1000research.16613.1

Open this paper in CoChat — free account Open PDF

Abstract

Background: Single-cell RNASeq is a powerful tool for measuring gene expression at the resolution of individual cells. A significant challenge in the analysis of this data is the large amount of zero values, representing either missing data or no expression. Several imputation approaches have been proposed to deal with this issue, but since these methods generally rely on structure inherent to the dataset under consideration they may not provide any additional information. Methods: We evaluated the risk of generating false positive or irreproducible results when imputing data with five different methods. We applied each method to a variety of simulated datasets as well as to permuted real single-cell RNASeq datasets and consider the number of false positive gene-gene correlations and differentially expressed genes. Using matched 10X Chromium and Smartseq2 data from the Tabula Muris database we examined the reproducibility of markers before and after imputation. Results: The extent of false-positive signals introduced by imputation varied considerably by method. Data smoothing based methods, MAGIC and knn-smooth, generated a very high number of false-positives in both real and simulated data. Model-based imputation methods typically generated fewer false-positives but this varied greatly depending on how well datasets conformed to the underlying model. Furthermore, only SAVER exhibited reproducibility comparable to unimputed data across matched data. Conclusions: Imputation of single-cell RNASeq data introduces circularity that can generate false-positive results. Thus, statistical tests applied to imputed data should be treated with care. Additional filtering by effect size can reduce but not fully eliminate these effects. Of the methods we considered, SAVER was the least likely to generate false or irreproducible results, thus should be favoured over alternatives if imputation is necessary.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Integrity signals

Registry

Crossref · 10.12688/f1000research.16613.1

Cites

36 of 38 references verified0 citation errors · 1 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing
Benjamini, Y., & Hochberg, Y. (1995) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1995.tb02031.x · Cited by 109,482
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879
Use of Ranks in One-Criterion Variance Analysis
Kruskal, W. H., & Wallis, W. A. (1952) · Journal of the American Statistical Association · doi:10.1080/01621459.1952.10483441 · Cited by 13,912
10 Years of GWAS Discovery: Biology, Function, and Translation
Visscher, P. M., Wray, N. R., Zhang, Q., Sklar, P., McCarthy, M. I., Brown, M. A., & Yang, J. (2017) · The American Journal of Human Genetics · doi:10.1016/j.ajhg.2017.06.005 · Cited by 3,719
Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris
The Tabula Muris Consortium, coordination, O., coordination, L., processing, O. C. A., sequencing, L. P. A., analysis, C. D., annotation, C. T., Writing group, Supplemental text writing group, & Principal investigators (2018) · Nature · doi:10.1038/s41586-018-0590-4 · Cited by 2,842
Molecular Architecture of the Mouse Nervous System
Zeisel, A., Hochgerner, H., Lönnerberg, P., Johnsson, A., Memic, F., van der Zwan, J., Häring, M., Braun, E., Borm, L. E., La Manno, G., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.021 · Cited by 2,552
Tackling the widespread and critical impact of batch effects in high-throughput data
Leek, J. T., Scharpf, R. B., Bravo, H. C., Simcha, D., Langmead, B., Johnson, W. E., Geman, D., Baggerly, K., & Irizarry, R. A. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2825 · Cited by 2,196
Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852
Recovering Gene Interactions from Single-Cell Data Using Data Diffusion
van Dijk, D., Sharma, R., Nainys, J., Yim, K., Kathail, P., Carr, A. J., Burdziak, C., Moon, K. R., Chaffer, C. L., Pattabiraman, D., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.061 · Cited by 1,829
Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680

Flagged first, then by citations

Show all 25 references

Sorted by citations

Items 101–198 of 198

scRNA-seq mixology: towards better benchmarking of single cell RNA-seq analysis methodsPreprint
Tian, L., Dong, X., Freytag, S., Cao, K. L., Su, S., JalalAbadi, A., Amann‐Zalcenstein, D., Weber, T. S., Seidi, A., Jabbari, J. S., et al. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/433102 · Cited by 11
Chromatin-accessibility estimation from single-cell ATAC data with scOpenPreprint
Li, Z., Kuppe, C., Ziegler, S., Cheng, M., Kabgani, N., Menzel, S., Zenke, M., Kramann, R., & Costa, I. G. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/865931 · Cited by 10
DeepIMAGER: Deeply Analyzing Gene Regulatory Networks from scRNA-seq Data
Zhou, X., Pan, J., Chen, L., Zhang, S., & Chen, Y. (2024) · Biomolecules · doi:10.3390/biom14070766 · Cited by 11
SpotGF: Denoising spatially resolved transcriptomics data using an optimal transport-based gene filtering algorithm
Du, L., Kang, J., Hou, Y. X., Sun, H., & Zhang, B. (2024) · Cell Systems · doi:10.1016/j.cels.2024.09.005 · Cited by 9
Are dropout imputation methods for scRNA-seq effective for scATAC-seq data?
Liu, Y., Zhang, J., Wang, S., Zeng, X., & Zhang, W. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab442 · Cited by 10
ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data
Malec, M., Kurban, H., & Dalkilic, M. M. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04814-8 · Cited by 10
An Unsupervised Strategy for Identifying Epithelial-Mesenchymal Transition State Metrics in Breast Cancer and Melanoma
Klinke, D. J., & Torang, A. (2020) · iScience · doi:10.1016/j.isci.2020.101080 · Cited by 10
Pathway Analysis Interpretation in the Multi-Omic Era
Ryan, W. G., Sahay, S., Vergis, J. M., Weistuch, C., Meller, J., & McCullumsmith, R. E. (2025) · BioTech · doi:10.3390/biotech14030058 · Cited by 10
Obstacles to Studying Alternative Splicing Using scRNA-seqPreprint
Ferguson-Smith, A., Westoby, J., Artemov, P., & Hemberg, M. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.17863/cam.44833 · Cited by 9
Self-citation
An Overview of Algorithms and Associated Applications for Single Cell RNA-Seq Data Imputation
Basharat, Z., Majeed, S., Saleem, H., Khan, I. A., & Yasmin, A. (2020) · Current Genomics · doi:10.2174/1389202921999200716104916 · Cited by 8
Analysis of Single-Cell Gene Pair Coexpression Landscapes by Stochastic Kinetic Modeling Reveals Gene-Pair Interactions in Development
(2020) · doi:10.3389/fgene.2019.01387 · Cited by 8
scRNA-seq: oh, the joys
Marx, V. (2024) · Nature Methods · doi:10.1038/s41592-024-02263-0 · Cited by 9
A comparative evaluation of handling missing data points and modalities in electronic health records
Bhandari, A., & Tyagi, S. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2026.106302 · Cited by 9
mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis
Liu, P., & Li, J. J. (2025) · Nature Communications · doi:10.1038/s41467-025-63626-5 · Cited by 7
Estimating Metastatic Risk of Pancreatic Ductal Adenocarcinoma at Single-Cell Resolution
Chen, S., Zhou, S., Huang, Y., Yuan, M., Lei, W., Chen, J., Lin, K., & Jiang, W. (2022) · International Journal of Molecular Sciences · doi:10.3390/ijms232315020 · Cited by 7
Epi-Impute: Single-Cell RNA-seq Imputation via Integration with Single-Cell ATAC-seq
Raevskiy, M., Yanvarev, V., Jung, S., del Sol, A., & Medvedeva, Y. A. (2023) · International Journal of Molecular Sciences · doi:10.3390/ijms24076229 · Cited by 8
scRecover : Discriminating True and False Zeros in Single‐Cell RNA ‐Seq Data for Imputation
Miao, Z., Lin, X., Li, J., Ho, J., Meng, Q., & Zhang, X. (2025) · Statistics in Medicine · doi:10.1002/sim.10334 · Cited by 7
Improvements Achieved by Multiple Imputation for Single-Cell RNA-Seq Data in Clustering Analysis and Differential Expression Analysis
Zhu, M., & Lai, Y. (2022) · Journal of Computational Biology · doi:10.1089/cmb.2021.0597 · Cited by 7
Single-cell eQTLGen Consortium: a personalized understanding of diseasePreprint
van der Wijst, M. G. P., de Vries, D. H., Groot, H. E., Trynka, G., Hon, C.-C., Nawijn, M. C., Idaghdour, Y., van der Harst, P., Ye, C., Powell, J. E., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1909.12550 · Cited by 7
Graph Convolutional Network-based Method for Clustering Single-cell RNA-seq DataPreprint
Zeng, Y., Lin, J., Zhou, X., Lu, Y., & Yang, Y. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.02.278804 · Cited by 7
Entropy sorting of single cell RNA sequencing data reveals the inner cell mass in the human pre-implantation embryoPreprint
Radley, A., Corujo-Simón, E., Nichols, J., Smith, A. G., & Dunn, S.-J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.04.08.487653 · Cited by 7
SGTB: A graph representation learning model combining transformer and BERT for optimizing gene expression analysis in spatial transcriptomics data
Liu, F., Ren, S., Li, J., Lv, H., Jiang, F., & Yu, B. (2025) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2025.108482 · Cited by 6
Benchmarking scRNA-seq imputation tools with respect to network inference highlights deficits in performance at high levels of sparsityPreprint
Steinheuer, L. M., Canzler, S., & Hackermüller, J. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.02.438193 · Cited by 5
Modeling microbiome-trait associations with taxonomy-adaptive neural networks
Jiang, Y., Aton, M., Zhu, Q., & Lu, Y. Y. (2025) · Microbiome · doi:10.1186/s40168-025-02080-3 · Cited by 9
Robust classification using average correlations as features (ACF)
Schumann, Y., Neumann, J. E., & Neumann, P. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05224-0 · Cited by 5
Reply to: Questioning whether the IgM Fc receptor (FcμR) is expressed by innate immune cells
Kubli, S. P., Ramachandran, P., Duncan, G. S., Brokx, R. D., & Mak, T. W. (2022) · Nature Communications · doi:10.1038/s41467-022-31226-2 · Cited by 5
Regulatory network-based imputation of dropouts in single-cell RNA sequencing data
Leote, A. C., Wu, X., & Beyer, A. (2022) · Kölner Universitäts PublikationsServer (Universität zu Köln) · Cited by 5
Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomicsPreprint
Choi, K., Chen, Y., Skelly, D. A., & Churchill, G. A. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.03.974808 · Cited by 5
scTsI: an effective two-stage imputation method for single-cell RNA-seq data
Zhang, H., Li, W., & Guan, J. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf298 · Cited by 5
Comparative genomic insights into multidrug resistance in classical and hypervirulent K. pneumoniae clinical isolates
Azra, Ullah, I., Hinthong, W., Phelan, J. E., Campino, S. G., Haq, I. U., Din, Z. U., Ahmad, S., Rani, G. F., Naz, A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-27122-6 · Cited by 4
eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings
Lin, K., Qiu, Y., & Roeder, K. (2024) · BMC Bioinformatics · doi:10.1186/s12859-024-05724-7 · Cited by 3
scGAAC: A graph attention autoencoder for clustering single-cell RNA-sequencing data
Zhang, L., Xiang, H., Wang, F., Chen, Z., Shen, M., Ma, J., Liu, H., & Zheng, H. (2024) · Methods · doi:10.1016/j.ymeth.2024.06.010 · Cited by 4
Recover then aggregate: unified cross-modal deep clustering with global structural information for single-cell data
Wang, Z., Luo, P., Xiao, M., Wang, B., Liu, T., & Sun, X. (2024) · Briefings in Bioinformatics · doi:10.1093/bib/bbae485 · Cited by 4
Augmented doubly robust post-imputation inference for proteomic data
Moon, H., Du, J., Lei, J., & Roeder, K. (2025) · The Annals of Applied Statistics · doi:10.1214/25-aoas2012 · Cited by 3
Imputation of single-cell transcriptome data enables the reconstruction of networks predictive of breast cancer metastasis
Cha, J., Lavi, M., Kim, J., Shomron, N., & Lee, I. (2023) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2023.03.036 · Cited by 6
Sprod for De-noising Spatial Transcriptomics Data Based on Position and Image InformationPreprint
Wang, Y., Song, B., Wang, S., Chen, M., Xie, Y., Xiao, G., Wang, L., & Wang, T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.03.467103 · Cited by 4
scAnnotate: an automated cell type annotation tool for single-cell RNA-sequencing dataPreprint
Ji, X., Tsao, D., Bai, K., Tsao, M., Xing, L., & Zhang, X. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.02.19.481159 · Cited by 4
Applications of AI to single-cell and spatial transcriptomics: current state-of-the-art and challenges
Ngassam, B. T., Niu, H., Pang, S., Shydlouskaya, V., & Andrews, T. S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2025.1715821 · Cited by 4
Self-citation
Regulatory network-based imputation of dropouts in single-cell RNA sequencing dataPreprint
Leote, A. C., Wu, X., & Beyer, A. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/611517 · Cited by 4
Single-cell network biology enabling cell-type-resolved disease genetics
Cha, J., & Lee, I. (2025) · Genomics & Informatics · doi:10.1186/s44342-025-00042-7 · Cited by 3
SAREV : A review on statistical analytics of single‐cell RNA sequencing data
Ellis, D. D., Wu, D., & Datta, S. (2021) · Wiley Interdisciplinary Reviews Computational Statistics · doi:10.1002/wics.1558 · Cited by 3
Using single-cell chromatin accessibility sequencing to characterize CD4+ T cells from murine tissues
Braband, K. L., Nedwed, A. S., Helbich, S. S., Simon, M., Beumer, N., Brors, B., Marini, F., & Delacher, M. (2023) · Frontiers in Immunology · doi:10.3389/fimmu.2023.1232511 · Cited by 3
Patterns, Profiles, and Parsimony: Dissecting Transcriptional Signatures From Minimal Single-Cell RNA-Seq Output With SALSA
Lozoya, O. A., McClelland, K. S., Papas, B. N., Li, J., & Yao, H. H. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.511286 · Cited by 2
Revealing the Best Strategies for Rare Cell Type Detection in Multi-Sample Single-Cell Datasets
Ye, Z., Yan, Y., Yu, Y., & Wu, H. S. (2025) · Genes · doi:10.3390/genes17010031 · Cited by 3
Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data
Fu, Y., Youness, M., Virzì, A., Song, X., Tubeeckx, M. R. L., De Keulenaer, G. W., Heidbuchel, H., Segers, V. F. M., Sipido, K. R., Thienpont, B., et al. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf371 · Cited by 2
“Inappropriate use of these published methods or incorrect selection of user settings can result in a bias in data analysis (such as by introducing artificial,non-biological signals) and false conclusions [41, 42].”
Tensor-Based Approaches for Omics Data Analysis: Applications, Challenges, and Future Directions
Khoshnam, A., Chafamo, D., & Tokcan, N. (2025) · La Matematica · doi:10.1007/s44007-025-00149-x · Cited by 2
SmartImpute is a targeted imputation framework for single-cell transcriptome data
Yao, S., Li, T., Davis, J. T., Shaw, T. I., Yu, X., & Wang, X. (2025) · Cell Reports Methods · doi:10.1016/j.crmeth.2025.101122 · Cited by 3
Inverse weighting method with jackknife variance estimator for differential expression analysis of single-cell RNA sequencing data
Zhou, L., & Pan, Q. (2022) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2022.107733 · Cited by 2
GPU-accelerated Kendall distance computation for large or sparse data
Akhtyamov, P., Nabi, A., Gafurov, V., Sizykh, A., Favorov, A. V., Medvedeva, Y. A., & Stupnikov, A. (2024) · GigaScience · doi:10.1093/gigascience/giae088 · Cited by 2
Protocol to denoise spatially resolved transcriptomics data utilizing optimal transport-based gene filtering algorithm
Du, L., Kang, J., Li, J., Qin, H., Hou, Y. X., & Sun, H. (2025) · STAR Protocols · doi:10.1016/j.xpro.2025.103625 · Cited by 2
Mapping regulators of cell fate determination: Approaches and challenges
Kumar, A., & Mali, P. (2020) · APL Bioengineering · doi:10.1063/5.0004611 · Cited by 2
EVALUATING THE COMPLEXITY OF GENE COEXPRESSION ESTIMATION FOR SINGLE-CELL DATA
Zhang, J., & Singh, R. (2023) · Journal of Machine Learning for Modeling and Computing · doi:10.1615/jmachlearnmodelcomput.2023047230 · Cited by 2
Factorization-based Imputation of Expression in Single-cell Transcriptomic Analysis (FIESTA) recovers Gene-Cell-State relationshipsPreprint
Mehrabad, E. M., Bhaskara, A., & Spike, B. T. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.04.29.441691 · Cited by 2
Evaluating imputation methods for accurate estimation of cell population fractions in single-cell RNA sequencing
Valyaeva, A. A., Tochilkina, M. S., & Sheval, E. V. (2025) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaf204 · Cited by 2
Revealing hidden regulatory dependencies: multi-perspective graph learning for single-cell gene regulatory network inference
He, W., Zhang, R., Zhu, Y., Zhou, H., Zuo, Y., Bai, Y., Yang, L., & Guo, F. (2025) · Briefings in Bioinformatics · doi:10.1093/bib/bbaf733 · Cited by 2
Beyond microbial abundance: metadata integration enhances disease prediction in human microbiome studies
Gonçalves, A. R., Ranganathan, H., Valdes, C., Zhu, H., Zhang, B., Kok, C. R., Martí, J. M., Mulakken, N., Thissen, J. B., Jaing, C. J., et al. (2026) · Frontiers in Microbiology · doi:10.3389/fmicb.2025.1695501 · Cited by 3
Augmenting the human interactome for disease prediction through gene networks inferred from human cell atlas
Sung, E., Cha, J., Baek, S., & Lee, I. (2025) · Animal Cells and Systems · doi:10.1080/19768354.2025.2472002 · Cited by 2
scSTATseq: Diminishing Technical Dropout Enables Core Transcriptome Recovery and Comprehensive Single-cell Trajectory MappingPreprint
Zheng, Z., Tang, X., Qiu, X., Xu, H., Wu, H., Yu, H., Wen, X., Peng, Z., Xu, F., Zhou, Y., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.04.15.042408 · Cited by 2
scCoBench: Benchmarking single cell RNA-seq co-expression using promoter-reporter linesPreprint
Chau, T. N., Ryu, K. H., Alajoleen, R. M., Bargmann, B. O. R., Schiefelbein, J., & Li, S. (2025) · Research Square · doi:10.1101/2025.05.26.656221 · Cited by 2
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Tianyi, S., Dongyuan, S., & Jessica Jingyi, L. (2020) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.4393040 · Cited by 2
Stochastic Analysis of the RT-PCR Process in Single-Cell RNA-Seq
Vázquez-Jiménez, A., & Reséndis-Antonio, O. (2021) · Mathematics · doi:10.3390/math9192515 · Cited by 1
Obstacles to Studying Alternative Splicing Using scRNA-seqPreprint
Westoby, J., Artemov, P., Hemberg, M., & Ferguson‐Smith, A. C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/797951 · Cited by 1
Self-citation
SwitchTFI: identifying transcription factors driving cell differentiation
Martini, P., Hartebrodt, A., de Almeida, G. P., Hackstein, C.-P., Zehn, D., & Blumenthal, D. B. (2025) · Genome biology · doi:10.1186/s13059-025-03876-0 · Cited by 2
Evaluating Model-free Directional Dependency Methods on Single-cell RNA Sequencing Data with Severe Dropout
Dvořáková, E., Kumar, S., Kléma, J., Železný, F., Drbal, K., & Song, M. J. (2019) · International Conference on Bioinformatics Research and Applications (ICBRA) · doi:10.1145/3383783.3383793 · Cited by 1
Faculty Opinions recommendation of A systematic evaluation of single cell RNA-seq analysis pipelines.
Hotz, H.-R. (2019) · Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature · doi:10.3410/f.736736304.793566365
ScLRTC: Imputation for Single-Cell RNA-Seq Data Via Low-Rank Tensor CompletionPreprint
Li, Z., Pan, X., Qin, S., Yu, M., & Hu, H. (2021) · Research Square · doi:10.21203/rs.3.rs-757792/v1
Context matters
de Vries, D. H. (2022) · University of Groningen research database (University of Groningen / Centre for Information Technology) · doi:10.33612/diss.255266474
Computational and Statistical Methods for Single-Cell RNA Sequencing Data
Wang, Z., & Yan, X. (2022) · Springer handbooks of computational statistics · doi:10.1007/978-3-662-65902-1_1
Optimal Transport Method-Based Gene Filter (GF) Denoising Algorithm for Enhancing Spatially Resolved Transcriptomics DataPreprint
Du, L., Kang, J., Sun, H., & Zhang, B. (2023) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2023.07.01.547049
Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritization
Shi, T., Yu, H., & Blair, R. H. (2023) · Statistical Applications in Genetics and Molecular Biology · doi:10.1515/sagmb-2022-0054
Towards Indonesian genbank: A framework for repository and discovery for nucleotide sequences in Indonesia
Mazaya, M., Syafarina, I., Hayati, A. K., Indrawati, A., Nugraha, I., Cartealy, I. C., Rosyidi, M., Bismantoko, S., Iryanto, S. B., Akbar, Z., et al. (2023) · AIP conference proceedings · doi:10.1063/5.0182737
Multiscale Network Approaches to Decode the Complexity of Common Human Diseases
Wang, M., Liu, Y., Schadt, E., & Zhang, B. (2025) · Oxford University Press eBooks · doi:10.1093/med/9780197640654.003.0002
Processing primary gene expression data: Normalization, harmonization and data quality control
Borisov, N. M., Sorokin, M., & Buzdin, A. A. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-19110-7.00017-3
cellSight: Characterizing dynamics of cells using single-cell RNA-sequencingPreprint
Chatterjee, R., Gohel, C., Taheriyoun, A. R., Shook, B. A., & Rahnavard, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.05.16.654572
Additional file 1 of Obstacles to detecting isoforms using full-length scRNA-seq data
Westoby, J., Artemov, P., Hemberg, M., & Ferguson-Smith, A. (2020) · Figshare · doi:10.6084/m9.figshare.12023475
Self-citation
Additional file 3 of eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings
Lin, K. Z., Qiu, Y., & Roeder, K. (2024) · Figshare · doi:10.6084/m9.figshare.26693686
Additional file 1 of DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
He, Y., Yuan, H., Wu, C., & Xie, Z. (2020) · Figshare · doi:10.6084/m9.figshare.12643853
Additional file 1 of ccImpute: an accurate and scalable consensus clustering based algorithm to impute dropout events in the single-cell RNA-seq data
Malec, M., Kurban, H., & Dalkilic, M. M. (2022) · Figshare · doi:10.6084/m9.figshare.20364792
Additional file 1 of eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings
Lin, K. Z., Qiu, Y., & Roeder, K. (2024) · Figshare · doi:10.6084/m9.figshare.26693680
Additional file 2 of eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings
Lin, K. Z., Qiu, Y., & Roeder, K. (2024) · Figshare · doi:10.6084/m9.figshare.26693683
Additional file 2 of DISC: a highly scalable and accurate inference of gene expression and structure for single-cell transcriptomes using semi-supervised deep learning
He, Y., Yuan, H., Wu, C., & Xie, Z. (2020) · Figshare · doi:10.6084/m9.figshare.12643856
Additional file 2 of Obstacles to detecting isoforms using full-length scRNA-seq data
Westoby, J., Artemov, P., Hemberg, M., & Ferguson-Smith, A. (2020) · Figshare · doi:10.6084/m9.figshare.12023481
Self-citation
Additional file 4 of eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings
Lin, K. Z., Qiu, Y., & Roeder, K. (2024) · Figshare · doi:10.6084/m9.figshare.26693689
Navigating Tumor Microenvironments with Spatial Transcriptomics for Effective Therapeutics
Krolla, C., Das, T., Tsouris, A., Chakraborty, S., & Andrieux, G. (2026) · Interdisciplinary biotechnological advances · doi:10.1007/978-981-95-3612-2_4
Emerging Technologies in Single-Cell Analysis
Thi, V. A. P., Nguyen, K. L., Thi, T.-H. N., & Chu, D.-T. (2026) · Springer eBooks · doi:10.1007/978-981-95-9789-5_2
DepthDiff: Restoring Low-Depth Single-Cell RNA-Seq Signals via Diffusion Denoising
Hou, X., Sun, J., Liu, Y., & Wang, G. (2026) · Biology · doi:10.3390/biology15151223
Dynamics of natural killer cell homeostasis : implications for cell-based cancer Immunotherapy
Pfefferle, A. (2019) · Figshare
Computational Analysis of Biomolecular Data for Medical Applications from Bulk to Single-cell
Zhu, K. (2020) · doi:10.7916/d8-ny50-yq12
OmicPioneer-sc: an integrated, interactive visualization environment for single-cell sequencing dataPreprint
Weinstein, J. N., Rohrdanz, M. A., Stucky, M., Melott, J. M., Ma, J., Mohanty, V., Manyam, G. C., Wakefield, C., Chen, K., Navin, N. E., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.10.31.363580
Reliable and accurate gene expression quantification with subpopulation structure-aware constraints for single-cell RNA sequencingPreprint
Tu, C.-C., & Hung, J. (2022) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2022.11.08.515740
Validation of Morphology-Guided Computational Enhancement for Single-Cell Resolution Spatial TranscriptomicsPreprint
Elbialy, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.07.03.663047
Additional file 1 of Robust classification using average correlations as features (ACF)
Schumann, Y., Neumann, J. E., & Neumann, P. (2024) · Figshare · doi:10.6084/m9.figshare.26576021
Additional file 2 of Robust classification using average correlations as features (ACF)
Schumann, Y., Neumann, J. E., & Neumann, P. (2024) · Figshare · doi:10.6084/m9.figshare.26576024
BARTsc identifies key transcriptional regulators from single-cell omics dataPreprint
Zhang, H., Kang, L., Wang, J., Liang, K., Wang, Z., Xu, K., & Zang, C. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.02.24.707729 · Cited by 1
Guidelines for single-cell RNA sequencing analysis of eosinophils
Handler, K., Gurtner, A., Raju, D., Gonzalez-Perez, I., & Arnold, I. C. (2026) · Journal of Leukocyte Biology · doi:10.1093/jleuko/qiag055 · Cited by 1
Auditable recovery of single-cell RNA-seq zeros with SPAREPreprint
Herńandez-Galaz, S., Pezoa, I., Hernández‐Oliveras, A., Rodriguez, S., Lladser, Á., & Martín, A. J. M. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.06.02.729664
Characterizing and mitigating protocol-dependent gene expression bias in 3′ and 5′ single-cell RNA sequencing
Shydlouskaya, V., Haeryfar, S. M. M., & Andrews, T. S. (2026) · Frontiers in Bioinformatics · doi:10.3389/fbinf.2026.1931243
Self-citation
GSNetAct: Task-aware network weighting for single-cell gene-set activity scoring
Garan, Z., & Kaymaz, Y. (2026) · Current Genomics · doi:10.1016/j.curgen.2026.10.004

Authors: Andrews, T. S., & Hemberg, M.
Affiliations: Wellcome Sanger Institute (GB)
Corresponding author: Hemberg, Martin
ORCID: Andrews, Tallulah S.
Hemberg, Martin
Venue: F1000Research
Publisher: F1000 Research Ltd
Published: 2018-11-02
Volume: 7
Pages: 1740
ISSN: 2046-1402 (ISSN-L)
DOI: 10.12688/f1000research.16613.1
OpenAlex: W4231157025
Cited by: 142
References: 25
Citations by year: 2026: 3 · 2025: 14 · 2024: 14 · 2023: 20 · 2022: 24 · 2021: 20 · 2020: 35 · 2019: 12
FWCI: 5.97
Type: journal article
Language: en
Update notices: new_version (2019-03-05)
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: F1000Research (publishedVersion, cc-by)
Funders: Wellcome Trust (SangerInstituteCoreFunding)
Topics: Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords: single-cell RNA sequencing, missing data imputation, false positives, zero values, gene-gene correlation, differentially expressed genes, savers, magic, model-based imputation, Tabula Muris

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

False signals induced by single-cell imputation

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Self-check

Citations

Cites

Cited by

Related

Metadata

Record

About these checks