A systematic evaluation of single cell RNA-seq analysis pipelines
Open accessPublished version of a preprint
Abstract
The recent rapid spread of single cell RNA sequencing (scRNA-seq) methods has created a large variety of experimental and computational pipelines for which best practices have not yet been established. Here, we use simulations based on five scRNA-seq library protocols in combination with nine realistic differential expression (DE) setups to systematically evaluate three mapping, four imputation, seven normalisation and four differential expression testing approaches resulting in ~3000 pipelines, allowing us to also assess interactions among pipeline steps. We find that choices of normalisation and library preparation protocols have the biggest impact on scRNA-seq analyses. Specifically, we find that library preparation determines the ability to detect symmetric expression differences, while normalisation dominates pipeline performance in asymmetric DE-setups. Finally, we illustrate the importance of informed choices by showing that a good scRNA-seq pipeline can have the same impact on detecting a biological signal as quadrupling the sample size.
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?
Papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent.
“Furthermore, papers evaluating the performance of single-cell-specific tools all compute the simulations as if cells were independent15–21.”
From A practical solution to pseudoreplication bias in single-cell studies (2021) · cites this paper as [15]
Scran normalization performs well for batch correction tasks.
“Scran performs well for batch correction tasks34,35”
From Best practices for single-cell analysis across modalities (2023) · cites this paper as [34]
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
Fast and accurate short read alignment with Burrows-Wheeler transform
Li, H., & Durbin, R. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp324 · Cited by 58,607 · see citing works
STAR: ultrafast universal RNA-seq aligner
Dobin, A., Davis, C. A., Schlesinger, F., Drenkow, J., Zaleski, C., Jha, S., Batut, P., Chaisson, M., & Gingeras, T. R. (2013) · Bioinformatics · doi:10.1093/bioinformatics/bts635 · Cited by 51,709 · see citing works
Modern Applied Statistics with S
Venables, W., & Ripley, B. (2010) · Statisctics and computing/Statistics and computing · doi:10.1007/978-0-387-21706-2 · Cited by 16,188 · see citing works
Differential expression analysis for sequence count data
Anders, S., & Huber, W. (2010) · Genome Biology · doi:10.1186/gb-2010-11-10-r106 · Cited by 15,581 · see citing works
Near-optimal probabilistic RNA-seq quantification
Bray, N. L., Pimentel, H., Melsted, P., & Pachter, L. (2016) · Nature Biotechnology · doi:10.1038/nbt.3519 · Cited by 9,723 · see citing works
A scaling normalization method for differential expression analysis of RNA-seq data
Robinson, M. D., & Oshlack, A. (2010) · Genome Biology · doi:10.1186/gb-2010-11-3-r25 · Cited by 7,876 · see citing works
Massively parallel digital transcriptional profiling of single cells
Zheng, G. X. Y., Terry, J. M., Belgrader, P., Ryvkin, P., Bent, Z. W., Wilson, R., Ziraldo, S. B., Wheeler, T. D., McDermott, G. P., Zhu, J., et al. (2017) · Nature Communications · doi:10.1038/ncomms14049 · Cited by 7,353 · see citing works
Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.
Macosko, E. Z., Basu, A., Satija, R., Nemesh, J., Shekhar, K., Goldman, M., Tirosh, I., Bialas, A. R., Kamitaki, N., Martersteck, E. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.002 · Cited by 7,110 · see citing works
O'Leary, N. A., Wright, M. W., Brister, J. R., Ciufo, S., Haddad, D., McVeigh, R., Rajput, B., Robbertse, B., Smith-White, B., Ako-Adjei, D., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1189 · Cited by 6,357 · see citing works
voom: precision weights unlock linear model analysis tools for RNA-seq read counts
Law, C. W., Chen, Y., Shi, W., & Smyth, G. K. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r29 · Cited by 6,155 · see citing works
Aran, D., Looney, A. P., Liu, L., Wu, E., Fong, V., Hsu, A., Chak, S., Naikawadi, R. P., Wolters, P. J., Abate, A. R., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0276-y · Cited by 4,984 · see citing works
Full-length RNA-seq from single cells using Smart-seq2
Picelli, S., Faridani, O. R., Björklund, Å. K., Winberg, G., Sagasser, S., & Sandberg, R. (2014) · Nature Protocols · doi:10.1038/nprot.2014.006 · Cited by 4,137 · see citing works
Finak, G., McDavid, A., Yajima, M., Deng, J., Gersuk, V., Shalek, A. K., Slichter, C. K., Miller, H. W., McElrath, M. J., Prlic, M., et al. (2015) · Genome Biology · doi:10.1186/s13059-015-0844-5 · Cited by 3,159 · see citing works
Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq
Zeisel, A., Muñoz-Manchado, A. B., Codeluppi, S., Lönnerberg, P., La Manno, G., Juréus, A., Marques, S., Munguba, H., He, L., Betsholtz, C., et al. (2015) · Science · doi:10.1126/science.aaa1934 · Cited by 3,124 · see citing works
GENCODE reference annotation for the human and mouse genomes
Frankish, A., Diekhans, M., Ferreira, A.-M., Johnson, R., Jungreis, I., Loveland, J. E., Mudge, J. M., Sisu, C., Wright, J. C., Armstrong, J. C., et al. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky955 · Cited by 2,929 · see citing works
Modern Applied Statistics with S Fourth edition
Venables, W. N., & Ripley, B. D. (2002) · Cited by 2,538 · see citing works
Cribari-Neto, F., & Zeileis, A. (2010) · Journal of Statistical Software · doi:10.18637/jss.v034.i02 · Cited by 2,505 · see citing works
Smith, T. S., Heger, A., & Sudbery, I. M. (2017) · Genome Research · doi:10.1101/gr.209601.116 · Cited by 2,072 · see citing works
Single-cell mRNA quantification and differential analysis with Census
Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,885 · see citing works
A better lemon squeezer? maximum-likelihood regression with beta-distributed dependent variables.
Smithson, M., & Verkuilen, J. (2006) · Psychological Methods · doi:10.1037/1082-989x.11.1.54 · Cited by 1,788 · see citing works
De novo assembly of bacterial transcriptomes from RNA-seq data
Tjaden, B. C. (2015) · Genome biology · doi:10.1186/s13059-014-0572-2 · Cited by 1,695 · see citing works
Comparative Analysis of Single-Cell RNA Sequencing Methods
Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works
Self-citationCEL-seq: single-cell RNA-seq by multiplexed linear amplification
Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231 · see citing works
Optimal classifier for imbalanced data using Matthews Correlation Coefficient metric
Boughorbel, S., Jarray, F., & El-Anbari, M. (2017) · PLOS ONE · doi:10.1371/journal.pone.0177678 · Cited by 1,222 · cited 1× · see citing works
“To summarise the whole confusion matrix in one representative value we chose the Matthews Correlation Coefficient ( MCC = ), because it is a balanced measure ensuring a reliable comparison of method performance across all DE-settings 45,46 .”
MethodPooling across cells to normalize single-cell RNA sequencing data with many zero counts
L. Lun, A. T., Bach, K., & Marioni, J. C. (2016) · Genome Biology · doi:10.1186/s13059-016-0947-7 · Cited by 1,108 · see citing works
GC-Content Normalization for RNA-Seq Data
Risso, D., Schwartz, K., Sherlock, G., & Dudoit, S. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-480 · Cited by 867 · see citing works
Bias, robustness and scalability in single-cell differential expression analysis
Soneson, C., & Robinson, M. D. (2018) · Nature Methods · doi:10.1038/nmeth.4612 · Cited by 770 · 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
Synthetic spike-in standards for RNA-seq experiments
Jiang, L., Schlesinger, F., Davis, C. A., Zhang, Y., Li, R., Salit, M., Gingeras, T. R., & Oliver, B. (2011) · Genome Research · doi:10.1101/gr.121095.111 · Cited by 653 · see citing works
Revealing the vectors of cellular identity with single-cell genomics
Wagner, A., Regev, A., & Yosef, N. (2016) · Nature Biotechnology · doi:10.1038/nbt.3711 · Cited by 632 · see citing works
Power analysis of single-cell RNA-sequencing experiments
Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568 · see citing works
The International Human Epigenome Consortium: A Blueprint for Scientific Collaboration and Discovery
Stunnenberg, H. G., Abrignani, S., Adams, D. J., de Almeida, M., Altucci, L., Amin, V. R., Amit, I., Antonarakis, S. E., Aparicio, S. A. J. R., Arima, T., et al. (2016) · Cell · doi:10.1016/j.cell.2016.11.007 · Cited by 485 · see citing works
Venables, W. N., & Ripley, B. D. (2002) · Statisctics and computing/Statistics and computing · doi:10.1007/978-0-387-21706-2_14 · Cited by 469 · 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
Selecting between-sample RNA-Seq normalization methods from the perspective of their assumptions
Evans, C., Hardin, J., & Stoebel, D. M. (2017) · Briefings in Bioinformatics · doi:10.1093/bib/bbx008 · Cited by 368 · see citing works
Droplet scRNA-seq is not zero-inflated
Svensson, V. (2019) · doi:10.1101/582064 · Cited by 356 · see citing works
The vertebrate genome annotation (Vega) database
Wilming, L. G., Gilbert, J. G. R., Howe, K., Trevanion, S. J., Hubbard, T., & Harrow, J. (2007) · Nucleic Acids Research · doi:10.1093/nar/gkm987 · Cited by 352 · see citing works
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs
Parekh, S., Ziegenhain, C., Vieth, B., Enard, W., & Hellmann, I. (2018) · GigaScience · doi:10.1093/gigascience/giy059 · Cited by 349 · see citing works
Self-citationDrImpute: 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
SCnorm: robust normalization of single-cell RNA-seq data
Bacher, R., Chu, L.-F., Leng, N., Gasch, A. P., Thomson, J. A., Stewart, R. M., Newton, M., & Kendziorski, C. (2017) · Nature Methods · doi:10.1038/nmeth.4263 · Cited by 282 · see citing works
Simulation-based comprehensive benchmarking of RNA-seq aligners
Baruzzo, G., Hayer, K. E., Kim, E. J., Camillo, B. D., FitzGerald, G., & Grant, G. (2016) · Nature Methods · doi:10.1038/nmeth.4106 · Cited by 261 · see citing works
Kim, J. K., Kolodziejczyk, A. A., Ilicic, T., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Communications · doi:10.1038/ncomms9687 · Cited by 251 · see citing works
Identifying cell populations with scRNASeq
Andrews, T. S., & Hemberg, M. (2017) · Molecular Aspects of Medicine · doi:10.1016/j.mam.2017.07.002 · Cited by 240 · see citing works
Observation weights unlock bulk RNA-seq tools for zero inflation and single-cell applications
Van den Berge, K., Perraudeau, F., Soneson, C., Love, M. I., Risso, D., Vert, J.-P., Robinson, M. D., Dudoit, S., & Clement, L. (2018) · Genome Biology · doi:10.1186/s13059-018-1406-4 · Cited by 228 · see citing works
Characterization of directed differentiation by high-throughput single-cell RNA-SeqPreprint
Soumillon, M., Cacchiarelli, D., Semrau, S., van Oudenaarden, A., & Mikkelsen, T. S. (2014) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/003236 · Cited by 217 · see citing works
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-Seq
Cole, M. B., Risso, D., Wagner, A., DeTomaso, D., Ngai, J., Purdom, E., Dudoit, S., & Yosef, N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.03.010 · Cited by 173 · see citing works
powsimR: power analysis for bulk and single cell RNA-seq experiments
Vieth, B., Ziegenhain, C., Parekh, S., Enard, W., & Hellmann, I. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx435 · Cited by 155 · see citing works
Self-citationFalse signals induced by single-cell imputation
Andrews, T. S., & Hemberg, M. (2018) · F1000Research · doi:10.12688/f1000research.16613.1 · Cited by 142 · see citing works
Zhao, S., & Zhang, B. (2015) · BMC Genomics · doi:10.1186/s12864-015-1308-8 · Cited by 130 · 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
Linnorm: improved statistical analysis for single cell RNA-seq expression data
Yip, S. H., Wang, P., Kocher, J.-P. A., Sham, P. C., & Wang, J. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx828 · Cited by 121 · see citing works
Pseudoalignment for metagenomic read assignment
Schaeffer, L., Pimentel, H., Bray, N., Melsted, P., & Pachter, L. S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx106 · Cited by 103 · see citing works
Limitations of alignment-free tools in total RNA-seq quantification
Wu, D. C., Yao, J., Ho, K. S., Lambowitz, A. M., & Wilke, C. O. (2018) · BMC Genomics · doi:10.1186/s12864-018-4869-5 · Cited by 83 · see citing works
Quantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 58 · see citing works
Self-citationiCOBRA: open, reproducible, standardized and live method benchmarking
Soneson, C., & Robinson, M. D. (2016) · Nature Methods · doi:10.1038/nmeth.3805 · Cited by 40 · see citing works
Kim, S., Kwak, W., Kim, H., Caetano-Anollés, K., Kim, S.-B., Kim, S.-B., Choi, K.-H., Kim, I., Hwang, J.-S., Kim, M., et al. (2017) · GigaScience · doi:10.1093/gigascience/gix113 · Cited by 37 · see citing works
Performance Assessment and Selection of Normalization Procedures for Single-Cell RNA-seqPreprint
Cole, M. B., Risso, D., Wagner, A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/235382 · Cited by 30 · see citing works
A mechanistic model for the negative binomial distribution of single-cell mRNA countsPreprint
Amrhein, L., Harsha, K., & Fuchs, C. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/657619 · Cited by 29 · see citing works
Flagged first, then by citations
1 of the 59 references couldn’t be matched to a known paper.
Cited by
Sorted by citations
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
Best practices for single-cell analysis across modalities
Heumos, L., Schaar, A. C., Lance, C., Litinetskaya, A., Drost, F., Zappia, L., Lücken, M. D., Strobl, D. C., Henao, J., Curion, F., et al. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00586-w · Cited by 1,051 · see citing works
“…was shown to work better for stabilizing variance for subsequent dimensionality reduction 33 , Scran performs well for batch correction tasks 34,35 , and analytical Pearson residuals are better suited for selection of biologically variable genes and identification of rare cell identities 31…”
BackgroundA Review of Explainable Artificial Intelligence in Healthcare
Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., et al. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109370 · Cited by 598 · see citing works
Silvin, A., Uderhardt, S., Piot, C., Da Mesquita, S., Yang, K., Geirsdottir, L., Mulder, K., Eyal, D., Liu, Z., Bridlance, C., et al. (2022) · Immunity · doi:10.1016/j.immuni.2022.07.004 · Cited by 368 · see citing works
A practical solution to pseudoreplication bias in single-cell studies
Zimmerman, K. D., Espeland, M. A., & Langefeld, C. D. (2021) · Nature Communications · doi:10.1038/s41467-021-21038-1 · Cited by 362 · see citing works
Single-cell and spatial transcriptomics: deciphering brain complexity in health and disease
Piwecka, M., Rajewsky, N., & Rybak-Wolf, A. (2023) · Nature Reviews Neurology · doi:10.1038/s41582-023-00809-y · Cited by 320 · 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
A 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
Single-cell analyses of aging, inflammation and senescence
Uyar, B., Palmer, D. H., Kowald, A., Escobar, H. M., Barrantes, I., Möller, S., Akalin, A., & Fuellen, G. (2020) · Ageing Research Reviews · doi:10.1016/j.arr.2020.101156 · Cited by 202 · see citing works
Applications of single-cell and bulk RNA sequencing in onco-immunology
Kuksin, M., Morel, D., Aglave, M., Danlos, F., Marabelle, A., Zinovyev, A. Y., Gautheret, D., & Verlingue, L. (2021) · European Journal of Cancer · doi:10.1016/j.ejca.2021.03.005 · Cited by 158 · see citing works
Germain, P., Sonrel, A., & Robinson, M. D. (2020) · Genome biology · doi:10.1186/s13059-020-02136-7 · Cited by 139 · see citing works
Cross-species RNA-seq for deciphering host–microbe interactions
Westermann, A. J., & Vogel, J. (2021) · Nature Reviews Genetics · doi:10.1038/s41576-021-00326-y · Cited by 136 · see citing works
Single cell transcriptome profiling of the human alcohol-dependent brain
Brenner, E., Tiwari, G. R., Kapoor, M., Liu, Y., Brock, A., & Mayfield, R. D. (2020) · Human Molecular Genetics · doi:10.1093/hmg/ddaa038 · Cited by 118 · see citing works
DIALOGUE maps multicellular programs in tissue from single-cell or spatial transcriptomics data
Jerby‐Arnon, L., & Regev, A. (2022) · Nature Biotechnology · doi:10.1038/s41587-022-01288-0 · Cited by 116 · see citing works
Gutierrez, C., Al’Khafaji, A. M., Brenner, E. A., Johnson, K. E., Gohil, S. H., Lin, Z., Knisbacher, B. A., Durrett, R., Li, S., Parvin, S., et al. (2021) · Nature Cancer · doi:10.1038/s43018-021-00222-8 · Cited by 106 · see citing works
Junttila, S., Smolander, J., & Elo, L. L. (2022) · Briefings in Bioinformatics · doi:10.1093/bib/bbac286 · Cited by 109 · see citing works
Johnson, K. A., & Krishnan, A. (2022) · Genome biology · doi:10.1186/s13059-021-02568-9 · Cited by 108 · see citing works
Genome-wide kinetic properties of transcriptional bursting in mouse embryonic stem cells
Ochiai, H., Hayashi, T., Umeda, M., Yoshimura, M., Harada, A., Shimizu, Y., Nakano, K., Saitoh, N., Kimura, H., Liu, Z., et al. (2020) · Science Advances · doi:10.1126/sciadv.aaz6699 · Cited by 105 · see citing works
Patch-seq: Past, Present, and Future
Lipovsek, M., Bardy, C., Cadwell, C. R., Hadley, K., Kobak, D., & Tripathy, S. J. (2021) · Journal of Neuroscience · doi:10.1523/jneurosci.1653-20.2020 · Cited by 98 · see citing works
Prime-seq, efficient and powerful bulk RNA sequencing
Janjic, A., Wange, L. E., Bagnoli, J., Geuder, J., Nguyen, P., Richter, D., Vieth, B., Vick, B., Jeremias, I., Ziegenhain, C., et al. (2022) · Genome biology · doi:10.1186/s13059-022-02660-8 · Cited by 101 · see citing works
Self-citationMegakaryocyte and Platelet Transcriptomics for Discoveries in Human Health and Disease
Davizon‐Castillo, P., Rowley, J. W., & Rondina, M. T. (2020) · Arteriosclerosis Thrombosis and Vascular Biology · doi:10.1161/atvbaha.119.313280 · Cited by 96 · see citing works
An AI Agent for Fully Automated Multi‐Omic Analyses
Zhou, J., Zhang, B., Li, G., Chen, X., Li, H., Xu, X., Chen, S., He, W., Xu, C., Liu, L., et al. (2024) · Advanced Science · doi:10.1002/advs.202407094 · 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
Koopmans, F., Li, K. W., Klaassen, R. V., & Smit, A. B. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00513 · Cited by 94 · see citing works
Sampling time-dependent artifacts in single-cell genomics studies
Massoni-Badosa, R., Iacono, G., Moutinho, C., Kulis, M., Palau, N., Marchese, D., Rodríguez‐Ubreva, J., Ballestar, E., Rodríguez-Esteban, G., Marsal, S., et al. (2020) · Genome biology · doi:10.1186/s13059-020-02032-0 · Cited by 82 · see citing works
Nkongolo, S., Mahamed, D., Kuipery, A., Vásquez, J. D. S., Kim, S., Mehrotra, A., Patel, A. B., Hu, C., McGilvray, I. D., Feld, J. J., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci158903 · Cited by 83 · see citing works
Single cell transcriptome research in human placenta
Li, H., Huang, Q., Liu, Y., & Garmire, L. X. (2020) · Reproduction · doi:10.1530/rep-20-0231 · Cited by 77 · see citing works
A benchmark study of simulation methods for single-cell RNA sequencing data
Cao, Y., Yang, P., & Yang, J. Y. H. (2021) · Nature Communications · doi:10.1038/s41467-021-27130-w · Cited by 74 · see citing works
Optimizing expression quantitative trait locus mapping workflows for single-cell studies
Cuomo, A. S. E., Alvari, G., Azodi, C. B. D., McCarthy, D. J., & Bonder, M. J. (2021) · Genome biology · doi:10.1186/s13059-021-02407-x · Cited by 71 · see citing works
Ko, K. I., Merlet, J., DerGarabedian, B. P., Huang, Z., Suzuki-Horiuchi, Y., Hedberg, M. L., Hu, E. Y., Nguyen, A., Prouty, S. M., Alawi, F., et al. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abj0324 · Cited by 76 · see citing works
Benchmarking UMI-based single-cell RNA-seq preprocessing workflows
You, Y., Tian, L., Su, S., Dong, X., Jabbari, J. S., Hickey, P. F., & Ritchie, M. E. (2021) · Genome biology · doi:10.1186/s13059-021-02552-3 · Cited by 72 · see citing works
Critical downstream analysis steps for single-cell RNA sequencing data
Zhang, Z., Cui, F., Lin, C., Zhao, L., Wang, C., & Zou, Q. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab105 · Cited by 70 · see citing works
Durán, R. C., González-Orozco, J. C., Velasco, I., & Wu, J. Q. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.884748 · Cited by 64 · see citing works
To the proteome and beyond: advances in single-cell omics profiling for plant systems
Clark, N. M., Elmore, J. M., & Walley, J. W. (2021) · PLANT PHYSIOLOGY · doi:10.1093/plphys/kiab429 · Cited by 66 · 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
Baruzzo, G., Cesaro, G., & Camillo, B. D. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac036 · Cited by 61 · see citing works
Quantitative single-cell transcriptomics
Ziegenhain, C., Vieth, B., Parekh, S., Hellmann, I., & Enard, W. (2018) · Briefings in Functional Genomics · doi:10.1093/bfgp/ely009 · Cited by 58 · see citing works
Self-citationSingle‐Cell Sequencing Methodologies: From Transcriptome to Multi‐Dimensional Measurement
Chen, Y., Song, J. T., Ruan, Q., Zeng, X., Wu, L., Cai, L., Wang, X., & Yang, C. J. (2021) · Small Methods · doi:10.1002/smtd.202100111 · Cited by 59 · see citing works
A hitchhiker's guide to single-cell transcriptomics and data analysis pipelines
Nayak, R., & Hasija, Y. (2021) · Genomics · doi:10.1016/j.ygeno.2021.01.007 · Cited by 58 · 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 53 · see citing works
Inflammatory Bowel Disease Through the Lens of Single-cell RNA-seq Technologies
Corridoni, D., Chapman, T., Antanaviciute, A., Satsangi, J. J., & Simmons, A. (2020) · Inflammatory Bowel Diseases · doi:10.1093/ibd/izaa089 · Cited by 54 · see citing works
Statistical 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
Maden, S. K., Kwon, S. H., Huuki-Myers, L. A., Collado‐Torres, L., Hicks, S. C., & Maynard, K. R. (2023) · Genome biology · doi:10.1186/s13059-023-03123-4 · Cited by 58 · see citing works
Evaluation of STAR and Kallisto on Single Cell RNA-Seq Data Alignment
Du, Y., Huang, Q., Arisdakessian, C. G., & Garmire, L. X. (2020) · G3 Genes Genomes Genetics · doi:10.1534/g3.120.401160 · Cited by 52 · see citing works
Does Gene Expression Noise Play a Functional Role in Plants?
Cortijo, S., & Locke, J. C. W. (2020) · Trends in Plant Science · doi:10.1016/j.tplants.2020.04.017 · Cited by 50 · see citing works
Consensus clustering of single-cell RNA-seq data by enhancing network affinity
Cui, Y., Zhang, S., Liang, Y. J., Wang, X., Ferraro, T. N., & Chen, Y. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab236 · Cited by 50 · see citing works
Riccetti, M., Gokey, J. J., Aronow, B. J., & Perl, A. T. (2020) · Matrix Biology · doi:10.1016/j.matbio.2020.05.002 · Cited by 50 · see citing works
Chen, L., Zhai, Y., He, Q., Wang, W., & Deng, M. (2020) · Genes · doi:10.3390/genes11070792 · Cited by 46 · see citing works
Angiogenesis in Lymph Nodes Is a Critical Regulator of Immune Response and Lymphoma Growth
Menzel, L., Höpken, U. E., & Rehm, A. (2020) · Frontiers in Immunology · doi:10.3389/fimmu.2020.591741 · Cited by 50 · 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
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
Comparative analysis of common alignment tools for single-cell RNA sequencing
Brüning, R. S., Tombor, L. S., Schulz, M. H., Dimmeler, S., & John, D. (2022) · GigaScience · doi:10.1093/gigascience/giac001 · Cited by 43 · see citing works
Machine learning for single-cell genomics data analysis
Raimundo, F., Meng-Papaxanthos, L., Vallot, C., & Vert, J. (2021) · Current Opinion in Systems Biology · doi:10.1016/j.coisb.2021.04.006 · Cited by 44 · see citing works
Toward a Consensus in the Repertoire of Hemocytes Identified in Drosophila
Cattenoz, P. B., Monticelli, S., Pavlidaki, A., & Giangrande, A. (2021) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2021.643712 · Cited by 39 · see citing works
Intraocular dendritic cells characterize HLA-B27-associated acute anterior uveitis
Kasper, M., Heming, M., Schafflick, D., Li, X., Lautwein, T., zu Horste, M. M., Bauer, D., Walscheid, K., Wiendl, H., Loser, K., et al. (2021) · eLife · doi:10.7554/elife.67396 · Cited by 38 · see citing works
Deep learning-based advances and applications for single-cell RNA-sequencing data analysis
Bao, S., Li, K., Yan, C., Zhang, Z., Qu, J., & Zhou, M. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab473 · Cited by 40 · see citing works
Soraggi, S., Riera, M., Meyts, E. R., Schierup, M. H., & Almstrup, K. (2020) · Human Genetics · doi:10.1007/s00439-020-02116-8 · Cited by 34 · see citing works
Göcz, B., Rumpler, É., Sárvári, M., Skrapits, K., Takács, S., Farkas, I., Csillag, V., Trinh, S. H., Bardóczi, Z., Ruska, Y., et al. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2113749119 · Cited by 35 · see citing works
Xiong, Z., Yang, Q., & Li, X. (2020) · CNS Neuroscience & Therapeutics · doi:10.1111/cns.13396 · Cited by 35 · see citing works
Johnson, K. E., Howard, G. R., Morgan, D., Brenner, E. A., Gardner, A. L., Durrett, R., Mo, W., Al’Khafaji, A. M., Sontag, E. D., Jarrett, A. M., et al. (2020) · Physical Biology · doi:10.1088/1478-3975/abb09c · Cited by 35 · see citing works
NECAB1 and NECAB2 are Prevalent Calcium-Binding Proteins of CB1/CCK-Positive GABAergic Interneurons
Miczán, V., Kelemen, K., Glavinics, J. R., László, Z. I., Barti, B., Kenesei, K., Kisfali, M., & Katona, I. (2020) · Cerebral Cortex · doi:10.1093/cercor/bhaa326 · Cited by 29 · see citing works
Sokolowski, D., Faykoo-Martinez, M., Erdman, L., Hou, H., Chan, C., Zhu, H., Holmes, M. M., Goldenberg, A., & Wilson, M. D. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab011 · Cited by 32 · see citing works
Canozo, F. J. G., Zuo, Z. C., Martin, J. F., & Samee, M. A. H. (2021) · Cell Systems · doi:10.1016/j.cels.2021.09.004 · Cited by 33 · see citing works
Perspectives on rigor and reproducibility in single cell genomics
Gibson, G. C. (2022) · PLoS Genetics · doi:10.1371/journal.pgen.1010210 · Cited by 34 · see citing works
Single-Cell Sequencing: Genomic and Transcriptomic Approaches in Cancer Cell Biology
Ortega-Batista, A., Jaén-Alvarado, Y., Moreno-Labrador, D., Gómez, N., García, G., & Guerrero, E. N. (2025) · International Journal of Molecular Sciences · doi:10.3390/ijms26052074 · Cited by 37 · 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
Cho, J., Kim, S., Lee, H., Rah, W., Cho, H. C., Kim, N. K., Bae, S., Shin, D. H., Lee, M. G., Park, I., et al. (2021) · Nature Biomedical Engineering · doi:10.1038/s41551-021-00783-0 · Cited by 33 · 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
Osthege, M., Tenhaef, N., Zyla, R., Müller, C., Hemmerich, J., Wiechert, W., Noack, S., & Oldiges, M. (2022) · Engineering in Life Sciences · doi:10.1002/elsc.202100108 · Cited by 32 · see citing works
Tuning parameters of dimensionality reduction methods for single-cell RNA-seq analysis
Raimundo, F., Vallot, C., & Vert, J. (2020) · Genome biology · doi:10.1186/s13059-020-02128-7 · Cited by 31 · see citing works
Li, M., Nishimura, T., Takeuchi, Y., Hongu, T., Wang, Y., Shiokawa, D., Wang, K., Hirose, H., Sasahara, A., Yano, M., et al. (2023) · Journal of Clinical Investigation · doi:10.1172/jci166666 · Cited by 30 · see citing works
Tfh Cells in Health and Immunity: Potential Targets for Systems Biology Approaches to Vaccination
Law, H. L., Venturi, V., Kelleher, A. D., & Munier, C. M. L. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21228524 · Cited by 29 · see citing works
CSEA-DB: an omnibus for human complex trait and cell type associations
Dai, Y., Hu, R., Manuel, A. M., Liu, A., Jia, P., & Zhao, Z. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa1064 · Cited by 30 · see citing works
Integration of Computational Analysis and Spatial Transcriptomics in Single-Cell Studies
Wang, R., Peng, G., Tam, P., & Jing, N. (2022) · Genomics Proteomics & Bioinformatics · doi:10.1016/j.gpb.2022.06.006 · Cited by 26 · see citing works
The circadian clock is disrupted in pancreatic cancer
Schwartz, P. B., Nukaya, M., Berres, M. E., Rubinstein, C. D., Wu, G., Hogenesch, J. B., Bradfield, C. A., & Ronnekleiv‐Kelly, S. M. (2023) · PLoS Genetics · doi:10.1371/journal.pgen.1010770 · Cited by 28 · see citing works
Thurman, A. L., Ratcliff, J. A., Chimenti, M. S., & Pezzulo, A. A. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab337 · Cited by 27 · see citing works
RNA recording in single bacterial cells using reprogrammed tracrRNAs
Jiao, C., Reckstadt, C., König, F., Homberger, C., Yu, J.-Q., Vogel, J., Westermann, A. J., Sharma, C., & Beisel, C. (2023) · Nature Biotechnology · doi:10.1038/s41587-022-01604-8 · Cited by 24 · see citing works
Meng, F.-L., Huang, X., Qin, W., Liu, K.-B., Wang, Y., Li, M., Ren, Y., Li, Y.-Z., & Sun, Y. (2023) · Biomarker Research · doi:10.1186/s40364-023-00523-3 · Cited by 27 · see citing works
Comparison of high-throughput single-cell RNA sequencing data processing pipelines
Gao, M., Ling, M., Tang, X., Wang, S., Xu, X., Qiao, Y., Yang, W., & Yu, R. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa116 · Cited by 24 · see citing works
Taming human brain organoids one cell at a time
Atamian, A., Cordón-Barris, L., & Quadrato, G. (2020) · Seminars in Cell and Developmental Biology · doi:10.1016/j.semcdb.2020.05.022 · Cited by 25 · see citing works
Zhang, X., Lai, H., Zhang, F., Wang, Y., Zhang, L., Yang, N., Wang, C., Liang, Z., Zeng, J., & Yang, J. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.635035 · Cited by 25 · see citing works
The accuracy of absolute differential abundance analysis from relative count data
Roche, K. E., & Mukherjee, S. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010284 · Cited by 22 · see citing works
Brandt, L., Cristinelli, S., & Ciuffi, A. (2020) · Annual Review of Virology · doi:10.1146/annurev-virology-021820-102458 · Cited by 25 · see citing works
Wang, L., Xie, Y., Chen, W., Zhang, Y., & Zeng, Y. (2021) · Animal Bioscience · doi:10.5713/ab.21.0092 · Cited by 24 · see citing works
COTAN: scRNA-seq data analysis based on gene co-expression
Galfrè, S. G., Morandin, F., Pietrosanto, M., Cremisi, F., & Helmer‐Citterich, M. (2021) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqab072 · Cited by 24 · see citing works
AdRoit is an accurate and robust method to infer complex transcriptome composition
Yang, T., Alessandri‐Haber, N., Fury, W., Schaner, M., Breese, R., LaCroix‐Fralish, M. L., Kim, J., Adler, C., Macdonald, L. E., Atwal, G. S., et al. (2021) · Communications Biology · doi:10.1038/s42003-021-02739-1 · Cited by 23 · see citing works
Mahdipour–Shirayeh, A., Erdmann, N., Leung-Hagesteijn, C., & Tiedemann, R. E. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab413 · 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
Differential expression analysis using a model-based gene clustering algorithm for RNA-seq data
Osabe, T., Shimizu, K., & Kadota, K. (2021) · BMC Bioinformatics · doi:10.1186/s12859-021-04438-4 · Cited by 24 · see citing works
Nault, R., Saha, S., Bhattacharya, S., Dodson, J. P., Sinha, S., Maiti, T., & Zacharewski, T. R. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac019 · Cited by 20 · see citing works
Borsi, G., Motheramgari, K., Dhiman, H., Baumann, M., Sinkala, E., Sauerland, M. B., Riba, J., & Borth, N. (2023) · Journal of Biotechnology · doi:10.1016/j.jbiotec.2023.01.006 · Cited by 20 · see citing works
Houser, A. E., Kazmi, A., Nair, A., & Ji, A. L. (2023) · JID Innovations · doi:10.1016/j.xjidi.2023.100198 · Cited by 22 · see citing works
An era of single-cell genomics consortia
Ando, Y., Kwon, A. T.-J., & Shin, J. W. (2020) · Experimental & Molecular Medicine · doi:10.1038/s12276-020-0409-x · Cited by 19 · see citing works
Yuan, F., Li, Y., Hu, R., Gong, M., Chai, M., Ma, X., Cha, J., Guo, P., Yang, K., Li, M., et al. (2023) · Molecular Psychiatry · doi:10.1038/s41380-023-01987-3 · Cited by 18 · see citing works
High-throughput full-length single-cell RNA-seq automation
Mamanova, L., Miao, Z., Jinat, A., Ellis, P., Shirley, L., & Teichmann, S. A. (2021) · Nature Protocols · doi:10.1038/s41596-021-00523-3 · Cited by 17 · see citing works
scGAD: a new task and end-to-end framework for generalized cell type annotation and discovery
Zhai, Y., Chen, L., & Deng, M. (2023) · Briefings in Bioinformatics · doi:10.1093/bib/bbad045 · Cited by 17 · see citing works
Uncovering functional lncRNAs by scRNA-seq with ELATUS
Goñi, E., Mas, A. M., González, J., Abad, A., Santisteban, M., Fortes, P., Huarte, M., & Hernáez, M. (2024) · Nature Communications · doi:10.1038/s41467-024-54005-7 · Cited by 16 · see citing works
Krzak, M., Alegbe, T., Taylor, D., Ghouraba, M., Strickland, M., Satti, R., Thompson, T., Arestang, K., Przybilla, M., Ramirez-Navarro, L., et al. (2023) · medRxiv · doi:10.1101/2023.09.06.23295056 · Cited by 3 · see citing works
Key Considerations on CITE‐Seq for Single‐Cell Multiomics
Song, H.-W., Martin, J., Shi, X., & Tyznik, A. J. (2025) · PROTEOMICS · doi:10.1002/pmic.202400011 · Cited by 18 · see citing works
Multi-Omics Profiling of the Tumor Microenvironment
Oekelen, O. V., & Laganà, A. (2022) · Advances in experimental medicine and biology · doi:10.1007/978-3-030-91836-1_16 · Cited by 13 · see citing works
- Authors
- Vieth, B., Parekh, S., Ziegenhain, C., Enard, W., & Hellmann, I.
- Affiliations
- Ludwig-Maximilians-Universität München (DE)
- Max Planck Institute for Biology of Ageing (DE)
- Karolinska Institutet (SE)
- Corresponding author
- Hellmann, Ines
- Venue
- Nature Communications
- Published
- 2019-10-11
- Volume
- 10
- Issue
- 1
- Pages
- 4667-4667
- ISSN
- 2041-1723 (ISSN-L)
- PMC
- PMC6789098
- OpenAlex
- W2980147288
- Cited by
- 265
- References
- 59
- Citations by year
- 2026: 10 · 2025: 13 · 2024: 24 · 2023: 41 · 2022: 38 · 2021: 82 · 2020: 55 · 2019: 1 · 2018: 1
- FWCI
- 15.38
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Nature Communications (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Open access LMU (Ludwid Maxmilian's Universitat Munchen) (submittedVersion, cc-by)
- Funders
- Deutsche Forschungsgemeinschaft (SFB 1243)
- 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, data analysis pipeline, differential gene expression, library preparation protocols, normalization, imputation, read mapping, simulated data, sample size, best practices
- MeSH terms
- RNA-Seq, Animals, Electronic Data Processing, Chromosome Mapping, Computer Simulation, Mice, Single-Cell Analysis
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
