False signals induced by single-cell imputation
Open accessAbstract
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
Cites
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?
Inappropriate use of published methods or incorrect selection of user settings can result in bias in data analysis and false conclusions.
“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].”
From Benchmarking of computational demultiplexing methods for single-nucleus RNA sequencing data (2025) · cites this paper as [41]
False correlation signals have been reported as a result of expression recovery.
“Indeed, false correlation signals have been reported as a result of expression recovery (Andrews & Hemberg, 2018).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as Andrews & Hemberg, 2018
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
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 · see citing works
An introduction to ROC analysis
Fawcett, T. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.10.010 · Cited by 21,879 · see citing works
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 · see citing works
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 · see citing works
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 3,326 · see citing works
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 2,995 · see citing works
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 · see citing works
McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · see citing works
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 · see citing works
Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works
SC3: consensus clustering of single-cell RNA-seq data
Kiselev, V. Y., Kirschner, K., Schaub, M. T., Andrews, T., Yiu, A., Chandra, T., Natarajan, K. N., Reik, W., Barahona, M., Green, A. R., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4236 · Cited by 1,500 · see citing works
Self-citationSingle-cell RNA-seq denoising using a deep count autoencoder
Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S., & Theis, F. J. (2019) · Nature Communications · doi:10.1038/s41467-018-07931-2 · Cited by 1,084 · see citing works
Splatter: simulation of single-cell RNA sequencing data
Zappia, L., Phipson, B., & Oshlack, A. (2017) · Genome Biology · doi:10.1186/s13059-017-1305-0 · Cited by 905 · see citing works
Moderated statistical tests for assessing differences in tag abundance
Robinson, M. D., & Smyth, G. K. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm453 · Cited by 856 · see citing works
SAVER: gene expression recovery for single-cell RNA sequencing
Huang, M. C., Wang, J., Torre, E. A., Dueck, H., Shaffer, S. M., Bonasio, R., Murray, J. I., Raj, A., Li, M., & Zhang, N. R. (2018) · Nature Methods · doi:10.1038/s41592-018-0033-z · Cited by 725 · see citing works
Validation of noise models for single-cell transcriptomics
Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works
An accurate and robust imputation method scImpute for single-cell RNA-seq data
Li, W. V., & Li, J. J. (2018) · Nature Communications · doi:10.1038/s41467-018-03405-7 · Cited by 682 · see citing works
The Human Cell Atlas: from vision to reality
Rozenblatt-Rosen, O., Stubbington, M. J. T., Regev, A., & Teichmann, S. A. (2017) · Nature · doi:10.1038/550451a · Cited by 638 · see citing works
Cell type atlas and lineage tree of a whole complex animal by single-cell transcriptomics
Plass, M., Solana, J., Wolf, F. A., Ayoub, S., Misios, A., Glažar, P., Obermayer, B., Theis, F. J., Kocks, C., & Rajewsky, N. (2018) · Science · doi:10.1126/science.aaq1723 · Cited by 474 · see citing works
Normalizing single-cell RNA sequencing data: challenges and opportunities
Vallejos, C. A., Risso, D., Scialdone, A., Dudoit, S., & Marioni, J. C. (2017) · Nature Methods · doi:10.1038/nmeth.4292 · Cited by 436 · see citing works
Mapping the Mouse Cell Atlas by Microwell-Seq
Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.012 · Cited by 384 · see citing works
DrImpute: imputing dropout events in single cell RNA sequencing data
Gong, W., Kwak, I., Pota, P., Koyano‐Nakagawa, N., & Garry, D. J. (2018) · BMC Bioinformatics · doi:10.1186/s12859-018-2226-y · Cited by 311 · see citing works
Wang, D., & Gu, J. (2018) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2018.08.003 · Cited by 212 · see citing works
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190 · see citing works
COXPRESdb: a database of coexpressed gene networks in mammals
Obayashi, T., Hayashi, S., Shibaoka, M., Saeki, M., Ohta, H., & Kinoshita, K. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm840 · Cited by 154 · see citing works
K-nearest neighbor smoothing for high-throughput single-cell RNA-Seq dataPreprint
Wagner, F., Yan, Y., & Yanai, I. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/217737 · Cited by 134 · see citing works
Comparison of Computational Methods for Imputing Single-Cell RNA-Sequencing Data
Zhang, L., & Zhang, S. (2018) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2018.2848633 · Cited by 125 · see citing works
McImpute: Matrix Completion Based Imputation for Single Cell RNA-seq Data
Mongia, A., Sengupta, D., & Majumdar, A. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00009 · Cited by 76 · see citing works
The Human Cell AtlasPreprint
Regev, A., Teichmann, S. A., Lander, E. S., Amit, I., Benoist, C. O., Birney, E., Bodenmiller, B., Campbell, P. J., Carninci, P., Clatworthy, M. R., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/121202 · Cited by 76 · see citing works
Self-citationHu, Q., & Greene, C. S. (2018) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789813279827_0033 · Cited by 68 · see citing works
Schaum, N., Karkanias, J., Neff, N. F., May, A. P., Quake, S. R., Wyss‐Coray, T., Darmanis, S., Batson, J., Botvinnik, O. B., Chen, M. B., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/237446 · Cited by 50 · see citing works
Chou, W., Zheng, H., Cheng, C., Yan, H., Wang, L., Han, F., Richards, J. B., Karasik, D., Kiel, D. P., & Hsu, Y. (2016) · Scientific Reports · doi:10.1038/srep39313 · Cited by 46 · see citing works
Hu, Q., & Greene, C. S. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/385534 · Cited by 44 · see citing works
Massive single-cell RNA-seq analysis and imputation via deep learningPreprint
Deng, Y. C., Bao, F., Dai, Q., Wu, L. F., & Altschuler, S. J. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/315556 · Cited by 21 · see citing works
Locality Sensitive Imputation for Single Cell RNA-Seq Data
Moussa, M. R., & Măndoiu, I. (2019) · Journal of Computational Biology · doi:10.1089/cmb.2018.0236 · Cited by 18 · see citing works
Single-cell RNA-seq data from microfluidic emulsion
The Tabula Muris Consortium (2017) · Figshare · doi:10.6084/m9.figshare.5715025 · Cited by 2 · see citing works
Single-cell RNA-seq data from Smart-seq2 sequencing of FACS sorted cells
The Tabula Muris Consortium (2017) · Figshare · doi:10.6084/m9.figshare.5715040 · Cited by 2 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Current best practices in single-cell RNA-seq analysis: a tutorial
Luecken, M. D., & Theis, F. J. (2019) · Molecular Systems Biology · doi:10.15252/msb.20188746 · Cited by 2,231 · see citing works
Eleven grand challenges in single-cell data science
Lähnemann, D., Köster, J., Szczurek, E., McCarthy, D. J., Hicks, S. C., Robinson, M. D., Vallejos, C. A., Campbell, K. R., Beerenwinkel, N., Mahfouz, A., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-1926-6 · Cited by 1,327 · see citing works
Orchestrating single-cell analysis with Bioconductor
Amezquita, R. A., Lun, A. T. L., Becht, E., Carey, V. J., Carpp, L. N., Geistlinger, L., Marini, F., Rue-Albrecht, K., Risso, D., Soneson, C., et al. (2020) · Nature Methods · doi:10.1038/s41592-019-0654-x · Cited by 945 · see citing works
Statistics or biology: the zero-inflation controversy about scRNA-seq data
Jiang, R., Sun, T., Song, D., & Li, J. J. (2022) · Genome Biology · doi:10.1186/s13059-022-02601-5 · Cited by 614 · see citing works
Mapping single-cell data to reference atlases by transfer learning
Lotfollahi, M., Naghipourfar, M., Luecken, M. D., Khajavi, M., Büttner, M., Wagenstetter, M., Avsec, Ž., Gayoso, A., Yosef, N., Interlandi, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01001-7 · Cited by 602 · see citing works
Joint probabilistic modeling of single-cell multi-omic data with totalVI
Gayoso, A., Steier, Z., Lopez, R., Regier, J., Nazor, K. L., Streets, A., & Yosef, N. (2021) · Nature Methods · doi:10.1038/s41592-020-01050-x · Cited by 592 · see citing works
Benchmarking single cell RNA-sequencing analysis pipelines using mixture control experiments
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. (2019) · Nature Methods · doi:10.1038/s41592-019-0425-8 · Cited by 342 · see citing works
Tutorial: guidelines for the computational analysis of single-cell RNA sequencing data
Andrews, T. S., Kiselev, V. Y., McCarthy, D. J., & Hemberg, M. (2020) · Nature Protocols · doi:10.1038/s41596-020-00409-w · Cited by 305 · see citing works
Self-citationA systematic evaluation of single-cell RNA-sequencing imputation methods
Hou, W., Ji, Z., Ji, H., & Hicks, S. C. (2020) · Genome biology · doi:10.1186/s13059-020-02132-x · Cited by 297 · see citing works
A systematic evaluation of single cell RNA-seq analysis pipelines
Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I. (2019) · Nature Communications · doi:10.1038/s41467-019-12266-7 · Cited by 265 · see citing works
Benchmarking Computational Doublet-Detection Methods for Single-Cell RNA Sequencing Data
Xi, N. M., & Li, J. J. (2021) · Cell Systems · doi:10.1016/j.cels.2020.11.008 · Cited by 208 · see citing works
The single-cell eQTLGen consortium
van der Wijst, M. G. P., de Vries, D., Groot, H. E., Trynka, G., Hon, C.-C., Bonder, M. J., Stegle, O., Nawijn, M. C., Idaghdour, Y., van der Harst, P., et al. (2020) · eLife · doi:10.7554/elife.52155 · Cited by 215 · see citing works
Single-cell transcriptomics unveils gene regulatory network plasticity
Iacono, G., Massoni-Badosa, R., & Heyn, H. (2019) · Genome Biology · doi:10.1186/s13059-019-1713-4 · Cited by 218 · see citing works
Data denoising with transfer learning in single-cell transcriptomics
Wang, J., Agarwal, D., Huang, M. C., Hu, G., Zhou, Z., Ye, C., & Zhang, N. R. (2019) · Nature Methods · doi:10.1038/s41592-019-0537-1 · Cited by 190 · see citing works
Gorin, G., Fang, M., Chari, T., & Pachter, L. (2022) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1010492 · Cited by 186 · see citing works
Naught all zeros in sequence count data are the same
Silverman, J. D., Roche, K. E., Mukherjee, S., & David, L. A. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.014 · Cited by 167 · see citing works
Single Cell RNA Sequencing in Atherosclerosis Research
Williams, J. W., Winkels, H., Durant, C. P., Zaitsev, K., Ghosheh, Y., & Ley, K. F. (2020) · Circulation Research · doi:10.1161/circresaha.119.315940 · Cited by 139 · see citing works
Single-cell network biology for resolving cellular heterogeneity in human diseases
Cha, J., & Lee, I. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-00528-0 · Cited by 139 · see citing works
Network inference with Granger causality ensembles on single-cell transcriptomics
Deshpande, A., Chu, L.-F., Stewart, R., & Gitter, A. (2022) · Cell Reports · doi:10.1016/j.celrep.2022.110333 · Cited by 130 · see citing works
A Review of Integrative Imputation for Multi-Omics Datasets
Song, M., Greenbaum, J., Luttrell, J., Zhou, W., Wu, C., Shen, H., Gong, P., Zhang, C., & Deng, H.-W. (2020) · Frontiers in Genetics · doi:10.3389/fgene.2020.570255 · Cited by 127 · see citing works
Chromatin-accessibility estimation from single-cell ATAC-seq data with scOpen
Li, Z., Kuppe, C., Ziegler, S. M., Cheng, M., Kabgani, N., Menzel, S., Zenke, M., Kramann, R., & Costa, I. G. (2021) · Nature Communications · doi:10.1038/s41467-021-26530-2 · Cited by 120 · see citing works
Tools for the analysis of high-dimensional single-cell RNA sequencing data
Wu, Y., & Zhang, K. (2020) · Nature Reviews Nephrology · doi:10.1038/s41581-020-0262-0 · Cited by 122 · see citing works
Tang, W., Bertaux, F., Thomas, P., Stefanelli, C., Saint, M., Marguerat, S., & Shahrezaei, V. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz726 · Cited by 113 · see citing works
Trzupek, D., Dunstan, M., Cutler, A. J., Lee, M., Godfrey, L., Jarvis, L., Rainbow, D. B., Aschenbrenner, D., Jones, J. L., Uhlig, H. H., et al. (2020) · Genome Medicine · doi:10.1186/s13073-020-00756-z · Cited by 105 · see citing works
Biologically informed deep learning to query gene programs in single-cell atlases
Lotfollahi, M., Rybakov, S., Hrovatin, K., Hediyeh-zadeh, S., Talavera-López, C., Misharin, A. V., & Theis, F. J. (2023) · Nature Cell Biology · doi:10.1038/s41556-022-01072-x · Cited by 100 · see citing works
Wang, M., Song, W., Chen, M., Wang, Q., Zhou, X., Xu, P., Krek, A., Yoon, Y., Ho, L., Orr, M. E., et al. (2022) · Molecular Neurodegeneration · doi:10.1186/s13024-022-00517-z · Cited by 100 · see citing works
Computational Methods for Single-Cell RNA Sequencing
Hie, B., Peters, J. M., Nyquist, S. K., Shalek, A. K., Berger, B., & Bryson, B. D. (2020) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-012220-100601 · Cited by 87 · see citing works
Chen, J., Cheong, C., Lan, L., Zhou, X., Liu, J., Lü, A., Cheung, W. K., & Zhang, L. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab325 · Cited by 88 · see citing works
Zeng, Y., Zhou, X., Rao, J., Lu, Y., & Yang, Y. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9313569 · Cited by 82 · see citing works
Surface protein imputation from single cell transcriptomes by deep neural networks
Zhou, Z., Ye, C., Wang, J., & Zhang, N. R. (2020) · Nature Communications · doi:10.1038/s41467-020-14391-0 · Cited by 83 · see citing works
Enhancing scientific discoveries in molecular biology with deep generative models
Lopez, R., Gayoso, A., & Yosef, N. (2020) · Molecular Systems Biology · doi:10.15252/msb.20199198 · Cited by 82 · see citing works
Bayesian model selection reveals biological origins of zero inflation in single-cell transcriptomics
Choi, K., Chen, Y., Skelly, D. A., & Churchill, G. A. (2020) · Genome biology · doi:10.1186/s13059-020-02103-2 · Cited by 77 · see citing works
Sprod for de-noising spatially resolved transcriptomics data based on position and image information
Wang, Y., Song, B., Wang, S., Chen, M., Xie, Y., Xiao, G., Wang, L., & Wang, T. (2022) · Nature Methods · doi:10.1038/s41592-022-01560-w · Cited by 85 · see citing works
Vértesy, Á., Eichmüller, O. L., Naas, J., Novatchkova, M., Esk, C., Balmaña, M., Ladstätter, S., Bock, C., von Haeseler, A., & Knoblich, J. A. (2022) · The EMBO Journal · doi:10.15252/embj.2022111118 · Cited by 70 · see citing works
Cho, J.-W., Park, S., Kim, G., Han, H., Shin, S., Shin, S., Bae, Y., Park, S. Y., Ha, S., Lee, I., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-26362-0 · Cited by 64 · see citing works
Considerations for building and using integrated single-cell atlases
Hrovatin, K., Sikkema, L., Shitov, V. A., Heimberg, G., Shulman, M., Oliver, A. J., Mueller, M. F., Ibarra, I. L., Wang, H., Ramírez-Suástegui, C., et al. (2024) · Nature Methods · doi:10.1038/s41592-024-02532-y · Cited by 67 · see citing works
scRMD: imputation for single cell RNA-seq data via robust matrix decomposition
Chen, C., Wu, C., Wu, L., Wang, X.-C., Deng, M., & Xi, R. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa139 · Cited by 63 · see citing works
A review of computational strategies for denoising and imputation of single-cell transcriptomic data
Patruno, L., Maspero, D., Craighero, F., Angaroni, F., Antoniotti, M., & Graudenzi, A. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa222 · Cited by 56 · see citing works
The imbalance between Type 17 T-cells and regulatory immune cell subsets in psoriasis vulgaris
Kim, J., Moreno, A., & Krueger, J. G. (2022) · Frontiers in Immunology · doi:10.3389/fimmu.2022.1005115 · Cited by 56 · see citing works
Obstacles to detecting isoforms using full-length scRNA-seq data
Westoby, J., Artemov, P., Hemberg, M., & Ferguson‐Smith, A. C. (2020) · Genome biology · doi:10.1186/s13059-020-01981-w · Cited by 51 · see citing works
Self-citationStatistical and Bioinformatics Analysis of Data from Bulk and Single-Cell RNA Sequencing Experiments
Yu, X., Aghababazadeh, F. A., Chen, Y. A., & Fridley, B. L. (2020) · Methods in molecular biology · doi:10.1007/978-1-0716-0849-4_9 · Cited by 54 · see citing works
Evaluating Proteomics Imputation Methods with Improved Criteria
Harris, L., Fondrie, W. E., Oh, S., & Noble, W. S. (2023) · Journal of Proteome Research · doi:10.1021/acs.jproteome.3c00205 · Cited by 52 · see citing works
Boyeau, P., Regier, J., Gayoso, A., Jordan, M. I., Lopez, R., & Yosef, N. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2209124120 · Cited by 56 · see citing works
Radley, A., Corujo-Simón, E., Nichols, J., Smith, A. G., & Dunn, S.-J. (2022) · Stem Cell Reports · doi:10.1016/j.stemcr.2022.09.007 · Cited by 46 · see citing works
Measuring and Modelling the Epithelial- Mesenchymal Hybrid State in Cancer: Clinical Implications
Jolly, M. K., Murphy, R. J., Bhatia, S., Whitfield, H. J., Redfern, A. D., Davis, M. J., & Thompson, E. W. (2021) · Cells Tissues Organs · doi:10.1159/000515289 · Cited by 46 · see citing works
c-CSN: Single-Cell RNA Sequencing Data Analysis by Conditional Cell-Specific Network
Li, L., Dai, H., Fang, Z., & Chen, L. (2021) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2020.05.005 · Cited by 46 · see citing works
Evaluating imputation methods for single-cell RNA-seq data
Yi, C. X., Ma, X., Yuan, L., Sun, Z., & Wang, P. (2023) · BMC Bioinformatics · doi:10.1186/s12859-023-05417-7 · Cited by 47 · see citing works
Wang, J., Zou, Q., & Lin, C. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab345 · Cited by 44 · see citing works
The shaky foundations of simulating single-cell RNA sequencing data
Crowell, H. L., Leonardo, S. X. M., Soneson, C., & Robinson, M. D. (2023) · Genome biology · doi:10.1186/s13059-023-02904-1 · Cited by 52 · see citing works
Resolution of the curse of dimensionality in single-cell RNA sequencing data analysis
Imoto, Y., Nakamura, T., Escolar, E. G., Yoshiwaki, M., Kojima, Y., Yabuta, Y., Katou, Y., Yamamoto, T., Hiraoka, Y., & Saitou, M. (2022) · Life Science Alliance · doi:10.26508/lsa.202201591 · Cited by 39 · see citing works
Single cell genomics as a transformative approach for aquaculture research and innovation
Daniels, R. R., Taylor, R. S., Robledo, D., & Macqueen, D. J. (2023) · Reviews in Aquaculture · doi:10.1111/raq.12806 · Cited by 35 · see citing works
Studying stochastic systems biology of the cell with single-cell genomics data
Gorin, G., Vastola, J. J., & Pachter, L. S. (2023) · Cell Systems · doi:10.1016/j.cels.2023.08.004 · Cited by 38 · see citing works
Longitudinal single-cell transcriptional dynamics throughout neurodegeneration in SCA1
Tejwani, L., Ravindra, N. G., Lee, C., Cheng, Y., Nguyen, B., Luttik, K. P., Ni, L., Zhang, S., Morrison, L. M., Gionco, J. T., et al. (2023) · Neuron · doi:10.1016/j.neuron.2023.10.039 · Cited by 36 · see citing works
Dissecting microbial communities with single-cell transcriptome analysis
Pountain, A. W., & Yanai, I. (2025) · Science · doi:10.1126/science.adp6252 · Cited by 37 · see citing works
scAnnotate: an automated cell-type annotation tool for single-cell RNA-sequencing data
Ji, X., Tsao, D., Bai, K., Tsao, M., Xing, L., & Zhang, X. (2023) · Bioinformatics Advances · doi:10.1093/bioadv/vbad030 · Cited by 36 · see citing works
He, Y., Yuan, H., Wu, C., & Xie, Z. (2020) · Genome biology · doi:10.1186/s13059-020-02083-3 · Cited by 35 · see citing works
From multitude to singularity: An up-to-date overview of scRNA-seq data generation and analysis
Carangelo, G., Magi, A., & Semeraro, R. (2022) · Frontiers in Genetics · doi:10.3389/fgene.2022.994069 · Cited by 36 · see citing works
Durán, R. C., Wei, H., & Wu, J. (2024) · BMC Genomics · doi:10.1186/s12864-024-10364-5 · Cited by 35 · see citing works
ESCO: single cell expression simulation incorporating gene co-expression
Tian, J., Wang, J., & Roeder, K. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab116 · Cited by 27 · see citing works
Accurate denoising of single-cell RNA-Seq data using unbiased principal component analysisPreprint
Wagner, F., Barkley, D., & Yanai, I. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/655365 · Cited by 30 · see citing works
Computational Methods for Data Integration and Imputation of Missing Values in Omics Datasets
Schumann, Y., Gocke, A., & Neumann, J. E. (2024) · PROTEOMICS · doi:10.1002/pmic.202400100 · Cited by 28 · see citing works
A clinical road map for single-cell omics
Skinnider, M. A., Courtine, G., Bloch, J., & Squair, J. W. (2025) · Cell · doi:10.1016/j.cell.2025.06.009 · Cited by 28 · see citing works
scDoc: correcting drop-out events in single-cell RNA-seq data
Ran, D., Zhang, S., Lytal, N., & An, L. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa283 · Cited by 27 · see citing works
A Bayesian framework for inter-cellular information sharing improves dscRNA-seq quantification
Srivastava, A., Malik, L. I., Sarkar, H., & Patro, R. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa450 · Cited by 25 · see citing works
Effect of imputation on gene network reconstruction from single-cell RNA-seq data
Ly, L.-H., & Vingron, M. (2021) · Patterns · doi:10.1016/j.patter.2021.100414 · Cited by 22 · see citing works
Zecharia, N., Krasnov, H., Vanunu, M., Castillo, A. I., Haberman, A., Dror, O., Vakal, L., Almeida, R. P. P., Blank, L., Shtienberg, D., et al. (2022) · Phytopathology · doi:10.1094/phyto-03-22-0105-r · Cited by 23 · see citing works
Defining the Skin Cellular Community Using Single-Cell Genomics to Advance Precision Medicine
Dubois, A., Gopee, N. H., Olabi, B., & Haniffa, M. A. (2020) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2020.05.104 · Cited by 23 · see citing works
Noise regularization removes correlation artifacts in single-cell RNA-seq data preprocessing
Zhang, R., Atwal, G. S., & Lim, W. K. (2021) · Patterns · doi:10.1016/j.patter.2021.100211 · Cited by 21 · see citing works
Cha, J., Yu, J., Cho, J.-W., Hemberg, M., & Lee, I. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac1042 · Cited by 19 · see citing works
Self-citationHausmann, F., Ergen, C., Khatri, R., Marouf, M., Hänzelmann, S., Gagliani, N., Huber, S., Machart, P., & Bonn, S. (2023) · Genome biology · doi:10.1186/s13059-023-03049-x · Cited by 21 · see citing works
Special function methods for bursty models of transcription
Gorin, G., & Pachter, L. S. (2020) · Physical review. E · doi:10.1103/physreve.102.022409 · Cited by 21 · see citing works
Data Denoising and Post-Denoising Corrections in Single Cell RNA Sequencing
Agarwal, D., Wang, J., & Zhang, N. R. (2020) · Statistical Science · doi:10.1214/19-sts7560 · Cited by 18 · see citing works
Ascensión, A. M., Araúzo-Bravo, M. J., & Izeta, A. (2022) · Life · doi:10.3390/life12010067 · Cited by 19 · see citing works
Regulatory network-based imputation of dropouts in single-cell RNA sequencing data
Leote, A. C., Wu, X., & Beyer, A. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009849 · Cited by 22 · see citing works
Charles, A. S., Falk, B., Turner, N. L., Pereira, T., Tward, D. J., Pedigo, B. D., Chung, J., Burns, R. C., Ghosh, S., Kebschull, J. M., et al. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-100119-110036 · Cited by 16 · see citing works
SCA: recovering single-cell heterogeneity through information-based dimensionality reduction
DeMeo, B., & Berger, B. (2023) · Genome biology · doi:10.1186/s13059-023-02998-7 · Cited by 19 · see citing works
Qin, J., Hu, Y., Yao, J., Leung, R. W. T., Zhou, Y., Qin, Y., & Wang, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab311 · Cited by 18 · see citing works
Guo, X., Ning, J., Chen, Y., Liu, G., Zhao, L., Fan, Y., & Sun, S. (2023) · Briefings in Functional Genomics · doi:10.1093/bfgp/elad011 · Cited by 18 · see citing works
Building and analyzing metacells in single-cell genomics data
Bilous, M., Hérault, L., Gabriel, A. A., Teleman, M., & Gfeller, D. (2024) · Molecular Systems Biology · doi:10.1038/s44320-024-00045-6 · Cited by 18 · see citing works
G2S3: A gene graph-based imputation method for single-cell RNA sequencing data
Wu, W., Dai, Q., Liu, Y., Yan, X., & Wang, Z. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009029 · Cited by 18 · see citing works
Clinical application of single‐cell RNA sequencing in disease and therapy
Nadukkandy, A. S., Kalaiselvan, S., Lin, L., & Luo, Y. (2025) · Clinical and Translational Medicine · doi:10.1002/ctm2.70512 · Cited by 18 · see citing works
Huang, M. C., Zhang, Z., & Zhang, N. R. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.08.03.234765 · Cited by 14 · see citing works
Meningeal immunity and neurological diseases: new approaches, new insights
Su, Y., Zheng, H., Shi, C., Li, X., Zhang, S., Guo, G., Yu, W.-K., Zhang, S., Hu, Z., Yang, J., et al. (2023) · Journal of Neuroinflammation · doi:10.1186/s12974-023-02803-z · Cited by 17 · see citing works
From Cell to Gene: Deciphering the Mechanism of Heart Failure With Single‐Cell Sequencing
Zhang, D., Wen, Q., Zhang, R., Kou, K., Lin, M., Zhang, S., Yang, J., Shi, H., Yang, Y., Tan, X., et al. (2024) · Advanced Science · doi:10.1002/advs.202308900 · Cited by 16 · see citing works
scLENS: data-driven signal detection for unbiased scRNA-seq data analysis
Kim, H., Chang, W., Chae, S. J., Park, J.-E., Seo, M., & Kim, J. K. (2024) · Nature Communications · doi:10.1038/s41467-024-47884-3 · Cited by 15 · see citing works
Qian, Y., Zou, Q., Zhao, M., Liu, Y., Guo, F., & Ding, Y. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012339 · Cited by 16 · see citing works
The progressive application of single-cell RNA sequencing technology in cardiovascular diseases
Yang, K., Huang, J.-Y., Zhou, P., Wang, Y., Xing, N., Yang, J., Lin, K.-X., Yi-fan, S., Lin, H.-B., & Li, R. (2022) · Biomedicine & Pharmacotherapy · doi:10.1016/j.biopha.2022.113604 · Cited by 16 · see citing works
Built on sand: the shaky foundations of simulating single-cell RNA sequencing dataPreprint
Crowell, H. L., Leonardo, S. X. M., Soneson, C., & Robinson, M. D. (2021) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2021.11.15.468676 · Cited by 16 · see citing works
Machine learning for profile prediction in genomics
Schreiber, J. M., & Singh, R. (2021) · Current Opinion in Chemical Biology · doi:10.1016/j.cbpa.2021.04.008 · Cited by 16 · see citing works
Lieberman, B., Kusi, M., Hung, C., Chou, C., Ning, H., Ho, Y., Taverna, J. A., Huang, T. H. ‐., & Chen, C. (2020) · Journal of Translational Genetics and Genomics · doi:10.20517/jtgg.2020.51 · Cited by 15 · see citing works
ScLRTC: imputation for single-cell RNA-seq data via low-rank tensor completion
Pan, X., Li, Z., Qin, S., Yu, M., & Hu, H. (2021) · BMC Genomics · doi:10.1186/s12864-021-08101-3 · Cited by 15 · see citing works
Zhang, S., Liu, Y., Yang, J., Lin, Z., & Ng, M. K. (2020) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqaa064 · Cited by 13 · see citing works
Zheng, Z., Qiu, X., Wu, H., Chang, L., Tang, X., Zou, L., Li, J., Wu, Y., Zhou, J., Jiang, S., et al. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab124 · Cited by 15 · see citing works
A Temporal Transcriptional Map of Human Natural Killer Cell DifferentiationPreprint
Pfefferle, A., Netskar, H., Ask, E. H., Lorenz, S., Goodridge, J. P., Sohlberg, E., Clancy, T., & Malmberg, K. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/630657 · Cited by 15 · see citing works
Joint learning of multiple gene networks from single-cell gene expression data
Wu, N., Fu, Y., Ou-Yang, L., Zhu, Z., & Xie, W. (2020) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2020.09.004 · Cited by 12 · see citing works
Hu, H., Liu, R., Zhao, C., Lu, Y., Xiong, Y., Chen, L., Jin, J., Ma, Y., Su, J., Yu, Z., et al. (2022) · RNA Biology · doi:10.1080/15476286.2022.2027151 · Cited by 13 · see citing works
Sparsity-Penalized Stacked Denoising Autoencoders for Imputing Single-Cell RNA-seq Data
Chi, W., & Deng, M. (2020) · Genes · doi:10.3390/genes11050532 · Cited by 13 · see citing works
Gorin, G., & Pachter, L. S. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.09.25.312868 · Cited by 13 · see citing works
Branching topology of the human embryo transcriptome revealed by Entropy Sort Feature Weighting
Radley, A., Boeing, S., & Smith, A. G. (2024) · Development · doi:10.1242/dev.202832 · Cited by 11 · see citing works
Dropout imputation and batch effect correction for single-cell RNA sequencing data
Li, G., Yang, Y., Buren, E. C. V., & Li, Y. (2019) · Journal of Bio-X Research · doi:10.1097/jbr.0000000000000053 · Cited by 12 · see citing works
- Authors
- Andrews, T. S., & Hemberg, M.
- Affiliations
- Wellcome Sanger Institute (GB)
- Corresponding author
- Hemberg, Martin
- Venue
- F1000Research
- Publisher
- F1000 Research Ltd
- Published
- 2018-11-02
- Volume
- 7
- Pages
- 1740
- ISSN
- 2046-1402 (ISSN-L)
- 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
- 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
