Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R
Open accessPublished version of a preprint
Abstract
Motivation: Single-cell RNA sequencing (scRNA-seq) is increasingly used to study gene expression at the level of individual cells. However, preparing raw sequence data for further analysis is not a straightforward process. Biases, artifacts and other sources of unwanted variation are present in the data, requiring substantial time and effort to be spent on pre-processing, quality control (QC) and normalization. Results: We have developed the R/Bioconductor package scater to facilitate rigorous pre-processing, quality control, normalization and visualization of scRNA-seq data. The package provides a convenient, flexible workflow to process raw sequencing reads into a high-quality expression dataset ready for downstream analysis. scater provides a rich suite of plotting tools for single-cell data and a flexible data structure that is compatible with existing tools and can be used as infrastructure for future software development. Availability and Implementation: The open-source code, along with installation instructions, vignettes and case studies, is available through Bioconductor at http://bioconductor.org/packages/scater. Supplementary information: Supplementary data are available at Bioinformatics online.
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?
In the manually sorted olfactory bulb dopaminergic neuron dataset, cell-level quality control and filtering was performed in scater.
“Cell level quality control and cell filtering was performed in scater (112).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [112]
In the analysis of scSeq datasets from various brain regions and sensory systems, inspection of all datasets for batch effects was performed using the scater package.
“Inspection of all datasets for batch effects was performed using the scater package (version 1.10.1) (112).”
From Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia (2020) · cites this paper as [112]
Popular platforms such as Seurat, Scater, or Scanpy limit themselves to tools developed in their respective programming languages.
“Popular platforms such as Seurat (Butler et al, 2018), Scater (McCarthy et al, 2017), or Scanpy (Wolf et al, 2018) provide integrated environments to develop pipelines and contain large analysis toolboxes. However, out of necessity these platforms limit themselves to tools developed in their respective programming languages.”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McCarthy et al, 2017
Among command line platforms, Scater and Seurat easily interface with the large variety of analysis tools available via the R Bioconductor project.
“Among command line platforms, Scater (McCarthy et al, 2017) and Seurat (Butler et al, 2018) easily interface with the large variety of analysis tools available via the R Bioconductor project (Huber et al, 2015).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McCarthy et al, 2017
Currently available platforms exist on the command line in R or Python, and as local applications or Web servers with graphical user interfaces (GUIs).
“Currently available platforms exist on the command line in R (McCarthy et al, 2017; Butler et al, 2018) or Python (Wolf et al, 2018), and as local applications (Patel, 2018; preprint: Scholz et al, 2018) or Web servers (Gardeux et al, 2017; Zhu et al, 2017) with graphical user interfaces (GUIs).”
From Current best practices in single-cell RNA-seq analysis: a tutorial (2019) · cites this paper as McCarthy et al, 2017
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 gapped-read alignment with Bowtie 2
Langmead, B., & Salzberg, S. L. (2012) · Nature Methods · doi:10.1038/nmeth.1923 · Cited by 56,400 · see citing works
Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works
Self-citationlimma powers differential expression analyses for RNA-sequencing and microarray studies
Ritchie, M. E., Phipson, B., Wu, D., Hu, Y., Law, C. W., Shi, W., & Smyth, G. K. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv007 · Cited by 40,458 · see citing works
(2008) · Journal of Machine Learning Research · Cited by 33,051 · see citing works
featureCounts: an efficient general purpose program for assigning sequence reads to genomic features
Liao, Y., Smyth, G. K., & Shi, W. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btt656 · Cited by 27,219 · see citing works
HTSeq—a Python framework to work with high-throughput sequencing data
Anders, S., Pyl, P. T., & Huber, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btu638 · Cited by 21,464 · 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
Kim, D., Pertea, G., Trapnell, C., Pimentel, H., Kelley, R., & Salzberg, S. L. (2013) · Genome Biology · doi:10.1186/gb-2013-14-4-r36 · Cited by 12,668 · 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
Trapnell, C., Cacchiarelli, D., Grimsby, J., Pokharel, P., Li, S., Morse, M., Lennon, N. J., Livak, K. J., Mikkelsen, T. S., & Rinn, J. L. (2014) · Nature Biotechnology · doi:10.1038/nbt.2859 · Cited by 7,314 · 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
Orchestrating high-throughput genomic analysis with Bioconductor
Huber, W. K., Carey, V. J., Gentleman, R. C., Anders, S., Carlson, M. R. J., Carvalho, B. S., Bravo, H. C., Davis, S. R., Gatto, L., Girke, T., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3252 · Cited by 3,499 · see citing works
Differential analysis of gene regulation at transcript resolution with RNA-seq
Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., & Pachter, L. (2013) · Nature Biotechnology · doi:10.1038/nbt.2450 · Cited by 3,492 · 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
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
Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis
Leek, J. T., & Storey, J. D. (2007) · PLoS Genetics · doi:10.1371/journal.pgen.0030161 · Cited by 1,930 · 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
A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.2 · Cited by 1,586 · see citing works
Self-citationAmir, E.-A. D., Davis, K. L., Tadmor, M. D., Simonds, E. F., Levine, J. H., Bendall, S. C., Shenfeld, D. K., Krishnaswamy, S., Nolan, G. P., & Pe'er, D. (2013) · Nature Biotechnology · doi:10.1038/nbt.2594 · Cited by 1,550 · 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
SC3 - consensus clustering of single-cell RNA-Seq dataPreprint
Kiselev, V., Kirschner, K., Schaub, M. T., et al. (2016) · Nature Methods · doi:10.1101/036558 · Cited by 1,357 · see citing works
Bayesian approach to single-cell differential expression analysis
Kharchenko, P. V., Silberstein, L., & Scadden, D. T. (2014) · Nature Methods · doi:10.1038/nmeth.2967 · Cited by 1,348 · see citing works
Yates, A. D., Akanni, W. A., Amode, M. R., Barrell, D. G., Billis, K., Carvalho‐Silva, D., Cummins, C. A., Clapham, P., Fitzgerald, S. F., Gil, L., et al. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1157 · Cited by 1,246 · see citing works
Single-cell messenger RNA sequencing reveals rare intestinal cell types
Grün, D., Lyubimova, A., Kester, L., Wiebrands, K., Basak, O., Sasaki, N., Clevers, H., & van Oudenaarden, A. (2015) · Nature · doi:10.1038/nature14966 · Cited by 1,234 · see citing works
Single-cell transcriptomics reveals bimodality in expression and splicing in immune cells
Shalek, A. K., Satija, R., Adiconis, X., Gertner, R. S., Gaublomme, J. T., Raychowdhury, R., Schwartz, S., Yosef, N., Malboeuf, C., Lu, D., et al. (2013) · Nature · doi:10.1038/nature12172 · Cited by 1,190 · see citing works
Buettner, F., Natarajan, K. N., Casale, F. P., Proserpio, V., Scialdone, A., Theis, F. J., Teichmann, S. A., Marioni, J. C., & Stegle, O. (2015) · Nature Biotechnology · doi:10.1038/nbt.3102 · Cited by 1,136 · see citing works
Pooling 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
Stegle, O., Parts, L., Piipari, M., Winn, J., & Durbin, R. (2012) · Nature Protocols · doi:10.1038/nprot.2011.457 · Cited by 1,049 · see citing works
Accounting for technical noise in single-cell RNA-seq experiments
Brennecke, P., Anders, S., Kim, J. K., Kołodziejczyk, A. A., Zhang, X., Proserpio, V., Baying, B., Benes, V., Teichmann, S. A., Marioni, J. C., et al. (2013) · Nature Methods · doi:10.1038/nmeth.2645 · Cited by 979 · see citing works
Bendall, S. C., Davis, K. L., Amir, E.-A. D., Tadmor, M. D., Simonds, E. F., Chen, T. J., Shenfeld, D. K., Nolan, G. P., & Pe’er, D. (2014) · Cell · doi:10.1016/j.cell.2014.04.005 · Cited by 906 · see citing works
A step-by-step workflow for low-level analysis of single-cell RNA-seq data
Lun, A. T. L., McCarthy, D. J., & Marioni, J. C. (2016) · F1000Research · doi:10.12688/f1000research.9501.1 · Cited by 789 · see citing works
Self-citationClassification of low quality cells from single-cell RNA-seq data
Ilicic, T., Kim, J. K., Kolodziejczyk, A. A., Bagger, F. O., McCarthy, D. J., Marioni, J. C., & Teichmann, S. A. (2016) · Genome Biology · doi:10.1186/s13059-016-0888-1 · Cited by 693 · see citing works
Self-citationDiffusion maps for high-dimensional single-cell analysis of differentiation data
Haghverdi, L., Buettner, F., & Theis, F. J. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv325 · Cited by 649 · see citing works
<i>destiny</i> : diffusion maps for large-scale single-cell data in R
Angerer, P., Haghverdi, L., Büttner, M., Theis, F. J., Marr, C., & Buettner, F. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btv715 · Cited by 640 · see citing works
svaseq: removing batch effects and other unwanted noise from sequencing data
Leek, J. T. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku864 · Cited by 538 · see citing works
Design and Analysis of Single-Cell Sequencing Experiments
Grün, D., & van Oudenaarden, A. (2015) · Cell · doi:10.1016/j.cell.2015.10.039 · Cited by 536 · see citing works
Outlier identification in high dimensions
Filzmoser, P., Maronna, R. A., & Werner, M. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.05.018 · Cited by 412 · see citing works
Computational assignment of cell-cycle stage from single-cell transcriptome data
Scialdone, A., Natarajan, K. N., Saraiva, L. R., Proserpio, V., Teichmann, S. A., Stegle, O., Marioni, J. C., & Buettner, F. (2015) · Methods · doi:10.1016/j.ymeth.2015.06.021 · Cited by 410 · see citing works
Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis
Fan, J., Salathia, N. S., Liu, R., Kaeser, G. E., Yung, Y. C., Herman, J. L., Kaper, F., Fan, J., Zhang, K., Chun, J. J. M., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3734 · Cited by 394 · see citing works
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis
Guo, M., Wang, H., Potter, S. S., Whitsett, J. A., & Xu, Y. J. (2015) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1004575 · Cited by 347 · see citing works
BASiCS: Bayesian Analysis of Single-Cell Sequencing Data
Vallejos, C. A., Marioni, J. C., & Richardson, S. (2015) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004333 · Cited by 302 · see citing works
Resolving early mesoderm diversification through single-cell expression profiling
Scialdone, A., Tanaka, Y., Jawaid, W., Moignard, V., Wilson, N. K., Macaulay, I. C., Marioni, J. C., & Göttgens, B. (2016) · Nature · doi:10.1038/nature18633 · Cited by 293 · see citing works
A statistical approach for identifying differential distributions in single-cell RNA-seq experiments
Korthauer, K. D., Chu, L.-F., Newton, M. A., Li, Y., Thomson, J., Stewart, R., & Kendziorski, C. (2016) · Genome Biology · doi:10.1186/s13059-016-1077-y · Cited by 273 · 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
A benchmark for RNA-seq quantification pipelines
Teng, M., Love, M. I., Davis, C., Djebali, S., Dobin, A., Graveley, B. R., Li, S., Mason, C. E., Olson, S., Pervouchine, D. D., et al. (2016) · Genome biology · doi:10.1186/s13059-016-0940-1 · Cited by 189 · see citing works
Beta-Poisson model for single-cell RNA-seq data analyses
Vu, T. N., Wills, Q. F., Kalari, K. R., Niu, N., Wang, L., Rantalainen, M., & Pawitan, Y. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw202 · Cited by 157 · see citing works
Self-citationKanitz, A., Gypas, F., Gruber, A. J., Gruber, A. J., Martin, G., & Zavolan, M. (2015) · Genome biology · doi:10.1186/s13059-015-0702-5 · Cited by 141 · see citing works
Patro, R., Duggal, G., Love, M. I., et al. (2015) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/021592 · Cited by 129 · see citing works
Chikina, M. D., Zaslavsky, E., & Sealfon, S. C. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv015 · Cited by 122 · see citing works
Normalization and noise reduction for single cell RNA-seq experiments
Ding, B., Zheng, L., Zhu, Y., Li, N., Jia, H., Ai, R., Wildberg, A., & Wang, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv122 · Cited by 99 · see citing works
Beyond comparisons of means: understanding changes in gene expression at the single-cell level
Vallejos, C. A., Richardson, S. T., & Marioni, J. C. (2016) · Genome biology · doi:10.1186/s13059-016-0930-3 · Cited by 94 · see citing works
Juliá, M., Telenti, A., & Rausell, A. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv368 · Cited by 55 · see citing works
Analysis and simulation of gene expression profiles in pure and mixed cell populations
Hebenstreit, D., & Teichmann, S. A. (2011) · Physical Biology · doi:10.1088/1478-3975/8/3/035013 · Cited by 20 · see citing works
Risso (2014) · doi:10.1038/nbt.2931
Flagged first, then by citations
Research works printed but not linked: 2
- Campbell K., Yau C. (2016) Ouija: Incorporating prior knowledge in single-cell trajectory learning using Bayesian nonlinear factor analysis. bioRxiv, 060442.
- Hicks S.C. et al (2015) On the widespread and critical impact of systematic bias and batch effects in single-cell RNA-Seq data. bioRxiv, 025528.
Cited by
Sorted by citations
SCANPY: large-scale single-cell gene expression data analysis
Wolf, F. A., Angerer, P., & Theis, F. J. (2018) · Genome Biology · doi:10.1186/s13059-017-1382-0 · Cited by 8,881 · see citing works
Scenic: single-cell regulatory network inference and clustering.
Aibar, S., González-Blas, C. B., Moerman, T., Huynh-Thu, V. A., Imrichova, H., Hulselmans, G., Rambow, F., Marine, J.-C., Geurts, P., Aerts, J., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4463 · Cited by 6,520 · see citing works
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
Resolving the fibrotic niche of human liver cirrhosis at single-cell level
Ramachandran, P., Dobie, R., Wilson-Kanamori, J. R., Dora, E. F., Henderson, B. E. P., Luu, N. T., Portman, J. R., Matchett, K. P., Brice, M., Marwick, J. A., et al. (2019) · Nature · doi:10.1038/s41586-019-1631-3 · Cited by 1,784 · see citing works
cGAS surveillance of micronuclei links genome instability to innate immunity
Mackenzie, K. J., Carroll, P., Martin, C.-A., Murina, O., Fluteau, A., Simpson, D. J., Olova, N., Sutcliffe, H., Rainger, J. K., Leitch, A., et al. (2017) · Nature · doi:10.1038/nature23449 · Cited by 1,752 · see citing works
Landscape and Dynamics of Single Immune Cells in Hepatocellular Carcinoma
Zhang, Q., He, Y., Luo, N., Patel, S. J., Han, Y., Gao, R., Modak, M., Carotta, S., Haslinger, C., Kind, D., et al. (2019) · Cell · doi:10.1016/j.cell.2019.10.003 · Cited by 1,624 · see citing works
Benchmarking atlas-level data integration in single-cell genomics
Luecken, M. D., Büttner, M., Chaichoompu, K., Danese, A., Interlandi, M., Mueller, M. F., Strobl, D. C., Zappia, L., Dugas, M., Colomé-Tatché, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-021-01336-8 · Cited by 1,438 · see citing works
Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex
Maynard, K. R., Collado-Torres, L., Weber, L. M., Uytingco, C., Barry, B. K., Williams, S. R., Catallini, J. L., Tran, M. N., Besich, Z., Tippani, M., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00787-0 · Cited by 1,222 · see citing works
Brann, D. H., Tsukahara, T., Weinreb, C., Lipovsek, M., Van den Berge, K., Gong, B., Chance, R., Macaulay, I. C., Chou, H.-J., Fletcher, R. B., et al. (2020) · Science Advances · doi:10.1126/sciadv.abc5801 · Cited by 1,198 · see citing works
A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications
Haque, A., Engel, J., Teichmann, S. A., & Lönnberg, T. (2017) · Genome Medicine · doi:10.1186/s13073-017-0467-4 · Cited by 1,071 · see citing works
Comprehensive functional genomic resource and integrative model for the human brain
Wang, D., Liu, S., Warrell, J., Won, H., Shi, X., Navarro, F. C. P., Clarke, D., Gu, M., Emani, P., Yang, Y. T., et al. (2018) · Science · doi:10.1126/science.aat8464 · Cited by 965 · see citing works
Spatial proteogenomics reveals distinct and evolutionarily conserved hepatic macrophage niches
Guilliams, M., Bonnardel, J., Haest, B., Vanderborght, B., Wagner, C., Remmerie, A., Bujko, A., Martens, L., Thoné, T., Browaeys, R., et al. (2022) · Cell · doi:10.1016/j.cell.2021.12.018 · Cited by 985 · see citing works
A Single-Cell Transcriptome Atlas of the Aging Drosophila Brain
Davie, K., Janssens, J., Koldere, D., De Waegeneer, M., Pech, U., Kreft, Ł., Aibar, S., Makhzami, S., Christiaens, V., Bravo González-Blas, C., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.057 · Cited by 840 · 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
Self-citationSplatter: 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
Self-Renewing Resident Cardiac Macrophages Limit Adverse Remodeling Following Myocardial Infarction
Dick, S. A., Macklin, J. A., Nejat, S., Momen, A., Clemente-Casares, X., Althagafi, M. G., Chen, J., Kantores, C., Hosseinzadeh, S., Aronoff, L., et al. (2019) · Nature Immunology · doi:10.1038/s41590-018-0272-2 · Cited by 883 · see citing works
Toward Minimal Residual Disease-Directed Therapy in Melanoma
Rambow, F., Rogiers, A., Marin-Bejar, O., Aibar, S., Femel, J., Dewaele, M., Karras, P., Brown, D., Chang, Y. H., Debiec-Rychter, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.06.025 · Cited by 771 · see citing works
A Python library for probabilistic analysis of single-cell omics data
Gayoso, A., Lopez, R., Xing, G., Boyeau, P., Valiollah Pour Amiri, V., Hong, J., Wu, K., Jayasuriya, M., Mehlman, E., Langevin, M., et al. (2022) · Nature Biotechnology · doi:10.1038/s41587-021-01206-w · Cited by 789 · see citing works
Structural Remodeling of the Human Colonic Mesenchyme in Inflammatory Bowel Disease
Kinchen, J., Chen, H. H., Parikh, K., Antanaviciute, A., Jagielowicz, M., Fawkner-Corbett, D., Ashley, N., Cubitt, L., Mellado-Gomez, E., Attar, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.08.067 · Cited by 745 · see citing works
Self-citationBias, 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
TREM2 regulates microglial cholesterol metabolism upon chronic phagocytic challenge
Nugent, A. A., Lin, K., van Lengerich, B., Lianoglou, S., Przybyla, L., Davis, S. S., Llapashtica, C., Wang, J., Kim, D. J., Xia, D., et al. (2020) · Neuron · doi:10.1016/j.neuron.2019.12.007 · Cited by 745 · see citing works
A general and flexible method for signal extraction from single-cell RNA-seq data
Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S., & Vert, J.-P. (2018) · Nature Communications · doi:10.1038/s41467-017-02554-5 · Cited by 718 · see citing works
Comparison and evaluation of statistical error models for scRNA-seq
Choudhary, S., & Satija, R. (2022) · Genome Biology · doi:10.1186/s13059-021-02584-9 · Cited by 634 · see citing works
Functional characterization of the dural sinuses as a neuroimmune interface
Rustenhoven, J., Drieu, A., Mamuladze, T., de Lima, K. A., Dykstra, T., Wall, M., Papadopoulos, Z., Kanamori, M., Salvador, A. F., Baker, W., et al. (2021) · Cell · doi:10.1016/j.cell.2020.12.040 · Cited by 685 · see citing works
Denisenko, E., Guo, B. B., Jones, M., Hou, R., de Kock, L., Lassmann, T., Poppe, D., Clément, O., Simmons, R. K., Lister, R., et al. (2020) · Genome Biology · doi:10.1186/s13059-020-02048-6 · Cited by 686 · see citing works
Dysregulation of brain and choroid plexus cell types in severe COVID-19
Yang, A. C., Kern, F., Losada, P. M., Agam, M. R., Maat, C. A., Schmartz, G. P., Fehlmann, T., Stein, J. A., Schaum, N., Lee, D. P., et al. (2021) · Nature · doi:10.1038/s41586-021-03710-0 · Cited by 620 · see citing works
Self-organization and symmetry breaking in intestinal organoid development
Serra, D., Mayr, U., Boni, A., Lukonin, I., Rempfler, M., Challet Meylan, L., Stadler, M. B., Strnad, P., Papasaikas, P., Vischi, D., et al. (2019) · Nature · doi:10.1038/s41586-019-1146-y · Cited by 561 · see citing works
CRISPR Interference-Based Platform for Multimodal Genetic Screens in Human iPSC-Derived Neurons
Tian, R., Gachechiladze, M. A., Ludwig, C. H., Laurie, M. T., Hong, J. Y., Nathaniel, D., Prabhu, A. V., Fernandopulle, M. S., Patel, R., Abshari, M., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.07.014 · Cited by 543 · see citing works
A test metric for assessing single-cell RNA-seq batch correction
Büttner, M., Miao, Z., Wolf, F. A., Teichmann, S. A., & Theis, F. J. (2019) · Nature Methods · doi:10.1038/s41592-018-0254-1 · Cited by 511 · see citing works
Rapid non-uniform adaptation to conformation-specific KRAS(G12C) inhibition
Xue, J. Y., Zhao, Y., Aronowitz, J., Mai, T. T., Vides, A., Qeriqi, B., Kim, D., Li, C., de Stanchina, E., Mazutis, L., et al. (2020) · Nature · doi:10.1038/s41586-019-1884-x · Cited by 543 · see citing works
Crowell, H. L., Soneson, C., Germain, P.-L., Calini, D., Collin, L., Raposo, C., Malhotra, D., & Robinson, M. D. (2020) · Nature Communications · doi:10.1038/s41467-020-19894-4 · Cited by 515 · see citing works
Benchmarking of cell type deconvolution pipelines for transcriptomics data
Avila Cobos, F., Alquicira-Hernandez, J., Powell, J. E., Mestdagh, P., & De Preter, K. (2020) · Nature Communications · doi:10.1038/s41467-020-19015-1 · Cited by 446 · see citing works
Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease
Leng, K., Li, E., Eser, R., Piergies, A., Sit, R., Tan, M., Neff, N., Li, S. H., Rodriguez, R. D., Suemoto, C. K., et al. (2021) · Nature Neuroscience · doi:10.1038/s41593-020-00764-7 · Cited by 485 · see citing works
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model
Townes, F. W., Hicks, S. C., Aryee, M. J., & Irizarry, R. A. (2019) · Genome Biology · doi:10.1186/s13059-019-1861-6 · Cited by 471 · see citing works
High-throughput single-cell ChIP-seq identifies heterogeneity of chromatin states in breast cancer
Grosselin, K., Durand, A., Marsolier, J., Poitou, A., Marangoni, E., Nemati, F., Dahmani, A., Lameiras, S., Reyal, F., Frenoy, O., et al. (2019) · Nature Genetics · doi:10.1038/s41588-019-0424-9 · Cited by 466 · see citing works
Li, B., Zhang, W., Guo, C., Xu, H., Li, L., Fang, M., Hu, Y., Zhang, X., Yao, X., Tang, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01480-9 · Cited by 459 · see citing works
Single-cell transcriptomics uncovers zonation of function in the mesenchyme during liver fibrosis
Dobie, R., Wilson-Kanamori, J. R., Henderson, B. E. P., Smith, J. R., Matchett, K. P., Portman, J. R., Wallenborg, K., Picelli, S., Zagorska, A., Pendem, S. V., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.10.024 · Cited by 420 · see citing works
Meningeal lymphatics affect microglia responses and anti-Aβ immunotherapy
Da Mesquita, S., Papadopoulos, Z., Dykstra, T., Brase, L., Farias, F. G., Wall, M., Jiang, H., Kodira, C. D., de Lima, K. A., Herz, J., et al. (2021) · Nature · doi:10.1038/s41586-021-03489-0 · Cited by 431 · see citing works
Meningeal γδ T cells regulate anxiety-like behavior via IL-17a signaling in neurons
Alves de Lima, K., Rustenhoven, J., Da Mesquita, S., Wall, M., Salvador, A. F., Smirnov, I., Martelossi Cebinelli, G., Mamuladze, T., Baker, W., Papadopoulos, Z., et al. (2020) · Nature Immunology · doi:10.1038/s41590-020-0776-4 · Cited by 416 · see citing works
<i>Enterococcus</i> peptidoglycan remodeling promotes checkpoint inhibitor cancer immunotherapy
Griffin, M. E., Espinosa, J., Becker, J. L., Luo, J.-D., Carroll, T. S., Jha, J. K., Fanger, G. R., & Hang, H. C. (2021) · Science · doi:10.1126/science.abc9113 · Cited by 366 · see citing works
Transcriptional signature in microglia associated with Aβ plaque phagocytosis
Grubman, A., Choo, X. Y., Chew, G., Ouyang, J. F., Sun, G., Croft, N. P., Rossello, F. J., Simmons, R., Buckberry, S., Landin, D. V., et al. (2021) · Nature Communications · doi:10.1038/s41467-021-23111-1 · Cited by 357 · see citing works
Joint analysis of heterogeneous single-cell RNA-seq dataset collections
Barkas, N., Petukhov, V., Nikolaeva, D., Lozinsky, Y., Demharter, S., Khodosevich, K., & Kharchenko, P. V. (2019) · Nature Methods · doi:10.1038/s41592-019-0466-z · Cited by 292 · see citing works
A blood atlas of COVID-19 defines hallmarks of disease severity and specificity
Ahern, D. J., Ai, Z., Ainsworth, M., Allan, C., Allcock, A., Angus, B., Ansari, M. A., Arancibia-Cárcamo, C. V., Aschenbrenner, D., Attar, M., et al. (2022) · Cell · doi:10.1016/j.cell.2022.01.012 · Cited by 328 · see citing works
Cuomo, A. S. E., Seaton, D. D., McCarthy, D. J., Martinez, I., Bonder, M. J., Garcia-Bernardo, J., Amatya, S., Madrigal, P., Isaacson, A., Buettner, F., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-14457-z · Cited by 331 · see citing works
Self-citationRuiz García, S., Deprez, M., Lebrigand, K., Cavard, A., Paquet, A., Arguel, M.-J., Magnone, V., Truchi, M., Caballero, I., Leroy, S., et al. (2019) · Development · doi:10.1242/dev.177428 · Cited by 297 · 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
A systematic performance evaluation of clustering methods for single-cell RNA-seq data
Duò, A., Robinson, M. D., & Soneson, C. (2018) · F1000Research · doi:10.12688/f1000research.15666.2 · Cited by 296 · see citing works
Single-cell technologies in hepatology: new insights into liver biology and disease pathogenesis
Ramachandran, P., Matchett, K. P., Dobie, R., Wilson-Kanamori, J. R., & Henderson, N. C. (2020) · Nature Reviews Gastroenterology & Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253 · see citing works
An autoimmune stem-like CD8 T cell population drives type 1 diabetes
Gearty, S. V., Dündar, F., Zumbo, P., Espinosa-Carrasco, G., Shakiba, M., Sanchez-Rivera, F. J., Socci, N. D., Trivedi, P., Lowe, S. W., Lauer, P., et al. (2022) · Nature · doi:10.1038/s41586-021-04248-x · Cited by 257 · see citing works
MUON: multimodal omics analysis framework
Bredikhin, D., Kats, I., & Stegle, O. (2022) · Genome Biology · doi:10.1186/s13059-021-02577-8 · Cited by 216 · see citing works
Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies
Lafzi, A., Moutinho, C., Picelli, S., & Heyn, H. (2018) · Nature Protocols · doi:10.1038/s41596-018-0073-y · Cited by 218 · see citing works
Tran, M. N., Maynard, K. R., Spangler, A., Huuki, L. A., Montgomery, K. D., Sadashivaiah, V., Tippani, M., Barry, B. K., Hancock, D. B., Hicks, S. C., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.09.001 · Cited by 219 · see citing works
Osorio, D., & Cai, J. J. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btaa751 · Cited by 212 · see citing works
Comparison of clustering tools in R for medium-sized 10x Genomics single-cell RNA-sequencing data
Freytag, S., Tian, L., Lönnstedt, I., Ng, M., & Bahlo, M. (2018) · F1000Research · doi:10.12688/f1000research.15809.1 · Cited by 220 · see citing works
Jackson, C. A., Castro, D. M., Saldi, G.-A., Bonneau, R., & Gresham, D. (2020) · eLife · doi:10.7554/elife.51254 · Cited by 174 · see citing works
He, L., Davila-Velderrain, J., Sumida, T. S., Hafler, D. A., Kellis, M., & Kulminski, A. M. (2021) · Communications Biology · doi:10.1038/s42003-021-02146-6 · Cited by 181 · see citing works
Da Mesquita, S., Herz, J., Wall, M., Dykstra, T., de Lima, K. A., Norris, G. T., Dabhi, N., Kennedy, T., Baker, W., & Kipnis, J. (2021) · Science Advances · doi:10.1126/sciadv.abe4601 · Cited by 172 · see citing works
Messmer, T., von Meyenn, F., Savino, A., Santos, F., Mohammed, H., Lun, A. T. L., Marioni, J. C., & Reik, W. (2019) · Cell Reports · doi:10.1016/j.celrep.2018.12.099 · Cited by 156 · see citing works
Self-citationPerformance 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
Directed evolution of adeno-associated virus for efficient gene delivery to microglia
Lin, R., Zhou, Y., Yan, T., Wang, R., Li, H., Wu, Z., Zhang, X., Zhou, X., Zhao, F., Zhang, L., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01547-7 · Cited by 171 · see citing works
Machado, L., Geara, P., Camps, J., Dos Santos, M., Teixeira-Clerc, F., Van Herck, J., Varet, H., Legendre, R., Pawlotsky, J.-M., Sampaolesi, M., et al. (2021) · Cell Stem Cell · doi:10.1016/j.stem.2021.01.017 · Cited by 170 · see citing works
Notch Signaling Mediates Secondary Senescence
Teo, Y. V., Rattanavirotkul, N., Olova, N., Salzano, A., Quintanilla, A., Tarrats, N., Kiourtis, C., Müller, M., Green, A. R., Adams, P. D., et al. (2019) · Cell Reports · doi:10.1016/j.celrep.2019.03.104 · Cited by 149 · see citing works
Single-cell RNA-seq uncovers a robust transcriptional response to morphine by glia
Avey, D., Sankararaman, S., Yim, A. K. Y., Barve, R., Milbrandt, J., & Mitra, R. D. (2018) · Cell Reports · doi:10.1016/j.celrep.2018.08.080 · Cited by 143 · see citing works
Complementary networks of cortical somatostatin interneurons enforce layer specific control
Naka, A., Veit, J., Shababo, B., Chance, R. K., Risso, D., Stafford, D., Snyder, B., Egladyous, A., Chu, D., Sridharan, S., et al. (2019) · eLife · doi:10.7554/elife.43696 · Cited by 117 · see citing works
False 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
Impact of similarity metrics on single-cell RNA-seq data clustering
Kim, T., Chen, I. R., Lin, Y., Wang, A. Y.-Y., Yang, J. Y. H., & Yang, P. (2019) · Briefings in Bioinformatics · doi:10.1093/bib/bby076 · Cited by 138 · see citing works
Single-cell genomics meets human genetics
Cuomo, A. S. E., Nathan, A., Raychaudhuri, S., MacArthur, D. G., & Powell, J. E. (2023) · Nature Reviews Genetics · doi:10.1038/s41576-023-00599-5 · Cited by 124 · see citing works
Clustering of single-cell multi-omics data with a multimodal deep learning method
Lin, X., Tian, T., Wei, Z., & Hakonarson, H. (2022) · Nature Communications · doi:10.1038/s41467-022-35031-9 · Cited by 135 · see citing works
Haematopoietic stem cells in perisinusoidal niches are protected from ageing
Saçma, M., Pospiech, J., Bogeska, R., de Back, W., Mallm, J.-P., Sakk, V., Soller, K., Marka, G., Vollmer, A., Karns, R., et al. (2019) · Nature Cell Biology · doi:10.1038/s41556-019-0418-y · Cited by 133 · see citing works
Valdeolivas, A., Amberg, B., Giroud, N., Richardson, M., Gálvez, E. J. C., Badillo, S., Julien-Laferrière, A., Túrós, D., Voith von Voithenberg, L., Wells, I., et al. (2024) · npj Precision Oncology · doi:10.1038/s41698-023-00488-4 · Cited by 130 · see citing works
Computational approaches for interpreting sc<scp>RNA</scp>‐seq data
Rostom, R., Svensson, V., Teichmann, S. A., & Kar, G. (2017) · FEBS Letters · doi:10.1002/1873-3468.12684 · Cited by 107 · see citing works
Martins-Ferreira, R., Calafell-Segura, J., Leal, B., Rodríguez-Ubreva, J., Martínez-Saez, E., Mereu, E., Pinho E Costa, P., Laguna, A., & Ballestar, E. (2025) · Nature Communications · doi:10.1038/s41467-025-56124-1 · Cited by 123 · see citing works
A comparison of marker gene selection methods for single-cell RNA sequencing data
Pullin, J. M., & McCarthy, D. J. (2022) · doi:10.1101/2022.05.09.490241 · Cited by 122 · see citing works
Self-citationDong, F., Hao, S., Zhang, S., Zhu, C., Cheng, H., Yang, Z., Hamey, F. K., Wang, X., Gao, A., Wang, F., et al. (2020) · Nature Cell Biology · doi:10.1038/s41556-020-0512-1 · Cited by 121 · see citing works
A visual–omics foundation model to bridge histopathology with spatial transcriptomics
Chen, W., Zhang, P., Tran, T. N., Xiao, Y., Li, S., Shah, V. V., Cheng, H., Brannan, K. W., Youker, K., Lai, L., et al. (2025) · Nature Methods · doi:10.1038/s41592-025-02707-1 · Cited by 118 · see citing works
Vickman, R. E., Broman, M. M., Lanman, N. A., Franco, O. E., Sudyanti, P. A. G., Ni, Y., Ji, Y., Helfand, B. T., Petkewicz, J., Paterakos, M. C., et al. (2020) · The Prostate · doi:10.1002/pros.23929 · Cited by 88 · see citing works
Association of a common genetic variant with Parkinson’s disease is mediated by microglia
Langston, R. G., Beilina, A., Reed, X., Kaganovich, A., Singleton, A. B., Blauwendraat, C., Gibbs, J. R., & Cookson, M. R. (2022) · Science Translational Medicine · doi:10.1126/scitranslmed.abp8869 · Cited by 93 · see citing works
Guo, J. L., Braun, D., Fitzgerald, G. A., Hsieh, Y.-T., Rougé, L., Litvinchuk, A., Steffek, M., Propson, N. E., Heffner, C. M., Discenza, C., et al. (2025) · Cell · doi:10.1016/j.cell.2024.10.027 · Cited by 91 · see citing works
Lotto, J., Drissler, S., Cullum, R., Wei, W., Setty, M., Bell, E. M., Boutet, S. C., Nowotschin, S., Kuo, Y.-Y., Garg, V., et al. (2020) · Cell · doi:10.1016/j.cell.2020.09.012 · Cited by 86 · see citing works
Hong, R., Koga, Y., Bandyadka, S., Leshchyk, A., Wang, Y., Akavoor, V., Cao, X., Sarfraz, I., Wang, Z., Alabdullatif, S., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-29212-9 · Cited by 82 · see citing works
Time-resolved single-cell RNA-seq using metabolic RNA labelling
Erhard, F., Saliba, A.-E., Lusser, A., Toussaint, C., Hennig, T., Prusty, B. K., Kirschenbaum, D., Abadie, K., Miska, E. A., Friedel, C. C., et al. (2022) · Nature Reviews Methods Primers · doi:10.1038/s43586-022-00157-z · Cited by 73 · see citing works
Valid Post-clustering Differential Analysis for Single-Cell RNA-Seq
Zhang, J. M., Kamath, G. M., & Tse, D. N. (2019) · Cell Systems · doi:10.1016/j.cels.2019.07.012 · Cited by 72 · see citing works
Midha, A., Pan, H., Abarca, C., Andle, J., Carapeto, P., Bonner-Weir, S., & Aguayo-Mazzucato, C. (2021) · Diabetes · doi:10.2337/db20-0553 · Cited by 58 · see citing works
Kannan, S., Farid, M., Lin, B. L., Miyamoto, M., & Kwon, C. (2021) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1009305 · Cited by 52 · see citing works
Zhu, X., Tian, X., Ji, L., Zhang, X., Cao, Y., Shen, C., Hu, Y., Wong, J. W. H., Fang, J.-Y., Hong, J., et al. (2021) · npj Precision Oncology · doi:10.1038/s41698-021-00142-x · Cited by 41 · see citing works
Single‐cell RNAseq provides insight into altered immune cell populations in human fracture nonunions
Avin, K. G., Dominguez, J. M., Chen, N. X., Hato, T., Myslinski, J. J., Gao, H., Liu, Y., McKinley, T. O., Brown, K. M., Moe, S. M., et al. (2023) · Journal of Orthopaedic Research · doi:10.1002/jor.25452 · Cited by 39 · see citing works
Perez-Shibayama, C., Gil-Cruz, C., Cadosch, N., Lütge, M., Cheng, H.-W., De Martin, A., Frischmann, K., Joachimbauer, A., Onder, L., Papadopoulou, I., et al. (2024) · Nature Cardiovascular Research · doi:10.1038/s44161-024-00432-0 · Cited by 35 · see citing works
SOX9 reprograms endothelial cells by altering the chromatin landscape
Fuglerud, B. M., Drissler, S., Lotto, J., Stephan, T. L., Thakur, A., Cullum, R., & Hoodless, P. A. (2022) · Nucleic Acids Research · doi:10.1093/nar/gkac652 · Cited by 35 · see citing works
Preman, P., Moechars, D., Fertan, E., Wolfs, L., Serneels, L., Shah, D., Lamote, J., Poovathingal, S., Snellinx, A., Mancuso, R., et al. (2024) · EMBO Molecular Medicine · doi:10.1038/s44321-024-00162-7 · Cited by 32 · see citing works
Lymphoid gene expression supports neuroprotective microglia function
Ayata, P., Crowley, J. M., Challman, M. F., Sahasrabuddhe, V., Gratuze, M., Werneburg, S., Ribeiro, D., Hays, E. C., Durán-Laforet, V., Faust, T. E., et al. (2025) · Nature · doi:10.1038/s41586-025-09662-z · Cited by 32 · see citing works
Single-Cell RNA Sequencing for Studying Human Cancers
Aran, D. (2023) · Annual Review of Biomedical Data Science · doi:10.1146/annurev-biodatasci-020722-091857 · Cited by 31 · 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
LKB1 drives stasis and C/EBP-mediated reprogramming to an alveolar type II fate in lung cancer
Murray, C. W., Brady, J. J., Han, M., Cai, H., Tsai, M. K., Pierce, S. E., Cheng, R., Demeter, J., Feldser, D. M., Jackson, P. K., et al. (2022) · Nature Communications · doi:10.1038/s41467-022-28619-8 · Cited by 22 · see citing works
Wang, W., Cen, Y., Lu, Z., Xu, Y., Sun, T., Xiao, Y., Liu, W., Li, J. J., & Wang, C. (2024) · Genome Biology · doi:10.1186/s13059-024-03284-w · Cited by 19 · see citing works
ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets
Zheng, Y., Caron, D. P., Kim, J. Y., Jun, S.-H., Tian, Y., Florian, M., Stuart, K. D., Sims, P. A., & Gottardo, R. (2022) · doi:10.1101/2022.04.29.489989 · Cited by 18 · see citing works
StabMap: Mosaic single cell data integration using non-overlapping features
Ghazanfar, S., Guibentif, C., & Marioni, J. C. (2022) · doi:10.1101/2022.02.24.481823 · Cited by 16 · see citing works
Cell diversity and plasticity during atrioventricular heart valve EMTs
Lotto, J., Cullum, R., Drissler, S., Arostegui, M., Garside, V. C., Fuglerud, B. M., Clement-Ranney, M., Thakur, A., Underhill, T. M., & Hoodless, P. A. (2023) · Nature Communications · doi:10.1038/s41467-023-41279-6 · Cited by 13 · see citing works
“Size-factor normalization, feature selection, dimensionality reduction, and doublet identi fi cation were performed using the scran and scater packages in R 82,83 .”
MethodExpression quantitative trait locus studies in the era of single-cell omics
Luo, J., Wu, X., Cheng, Y., Chen, G., Wang, J., & Song, X. (2023) · Frontiers in Genetics · doi:10.3389/fgene.2023.1182579 · Cited by 11 · see citing works
Cardiac differentiation roadmap for analysis of plasticity and balanced lineage commitment
Snabel, R. R., Cofiño-Fabrés, C., Baltissen, M., Schwach, V., Passier, R., & Veenstra, G. J. C. (2025) · Stem Cell Reports · doi:10.1016/j.stemcr.2025.102422 · Cited by 10 · see citing works
Demultiplexing of single-cell RNA-sequencing data using interindividual variation in gene expression
Nassiri, I., Kwok, A. J., Bhandari, A., Bull, K. R., Garner, L. C., Klenerman, P., Webber, C., Parkkinen, L., Lee, A. W., Wu, Y., et al. (2024) · Bioinformatics Advances · doi:10.1093/bioadv/vbae085 · Cited by 8 · see citing works
- Authors
- McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F.
- Affiliations
- European Bioinformatics Institute (GB)
- Centre for Human Genetics (GB)
- University of Cambridge (GB)
- Corresponding author
- McCarthy, Davis J
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-01-14
- Volume
- 33
- Issue
- 8
- Pages
- 1179-1186
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- PMC
- PMC5408845
- OpenAlex
- W2951158909
- Cited by
- 1,852
- References
- 56
- Citations by year
- 2026: 188 · 2025: 247 · 2024: 261 · 2023: 275 · 2022: 248 · 2021: 213 · 2020: 163 · 2019: 142 · 2018: 88 · 2017: 22 · 2016: 2 · 2014: 1 · 2012: 1
- FWCI
- 44.02
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Minerva Access (University of Melbourne) (submittedVersion, cc-by)
- Apollo (University of Cambridge) (submittedVersion, cc-by)
- Apollo (cc-by)
- Funders
- National Health and Medical Research Council (APP1112, 681 to D.J.M.)
- European Molecular Biology Laboratory (D.J.M.)
- Cancer Research UK (A1719, 7 to A.T.L.L.)
- United Kingdom Medical Research Council (K.R.C.)
- Oxford Single Cell Biology Consortium (Q.F.W.)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Keywords
- single-cell RNA sequencing, R/Bioconductor package, data preprocessing, quality control, normalization, data visualization, gene expression data, sequencing artifacts
- MeSH terms
- Cell Line, Humans, Programming Languages, Quality Control, RNA, Software, Statistics as Topic, Sequence Analysis, RNA, Principal Component Analysis, 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