Scater: Pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R

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

<tt>edgeR</tt> : a Bioconductor package for differential expression analysis of digital gene expression data

Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btp616 · Cited by 41,364 · see citing works

limma 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

ggplot2

Wickham, H. (2016) · Use R! · doi:10.1007/978-3-319-24277-4 · Cited by 33,612 · see citing works

Untitled work

(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

Salmon provides fast and bias-aware quantification of transcript expression

Patro, R., Duggal, G., Love, M. I., Irizarry, R. A., & Kingsford, C. (2017) · Nature Methods · doi:10.1038/nmeth.4197 · Cited by 12,700 · see citing works

TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.

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

The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells

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

MAST: a flexible statistical framework for assessing transcriptional changes and characterizing heterogeneity in single-cell RNA sequencing data

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

Normalization of RNA-seq data using factor analysis of control genes or samples

Risso, D., Ngai, J., Speed, T. P., & Dudoit, S. (2014) · Nature Biotechnology · doi:10.1038/nbt.2931 · Cited by 2,169 · 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

Evaluation of statistical methods for normalization and differential expression in mRNA-Seq experiments

Bullard, J. H., Purdom, E., Hansen, K. D., & Dudoit, S. (2010) · BMC Bioinformatics · doi:10.1186/1471-2105-11-94 · Cited by 1,680 · 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

viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

Amir, 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

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 1,511 · 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

Diffusion pseudotime robustly reconstructs lineage branching

Haghverdi, L., Büttner, M., Wolf, F. A., Buettner, F., & Theis, F. J. (2016) · Nature Methods · doi:10.1038/nmeth.3971 · Cited by 1,385 · 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

Ensembl 2016

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

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

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

Using probabilistic estimation of expression residuals (PEER) to obtain increased power and interpretability of gene expression analyses

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

Single-Cell Trajectory Detection Uncovers Progression and Regulatory Coordination in Human B Cell Development

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

Classification 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

Diffusion 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

Missing data and technical variability in single-cell RNA-sequencing experiments

Hicks, S. C., Townes, F. W., Teng, M., & Irizarry, R. A. (2018) · Biostatistics · doi:10.1093/biostatistics/kxx053 · Cited by 635 · 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

Peter Filzmoser, A., Maronna, R. A., & Werner, M. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2007.05.018 · Cited by 401 · 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

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 386 · 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

Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical stochastic allelic expression

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

Comparative assessment of methods for the computational inference of transcript isoform abundance from RNA-seq data

Kanitz, 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

Salmon provides accurate, fast, and bias-aware transcript expression estimates using dual-phase inferencePreprint

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

CellCODE: a robust latent variable approach to differential expression analysis for heterogeneous cell populations

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

Sincell : an R/Bioconductor package for statistical assessment of cell-state hierarchies from single-cell RNA-seq

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

A descriptive marker gene approach to single-cell pseudotime inferencePreprint

Campbell, K. R., & Yau, C. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/060442 · Cited by 15 · see citing works

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

Dictionary learning for integrative, multimodal and scalable single-cell analysis

Hao, Y., Stuart, T., Kowalski, M. H., Choudhary, S., Hoffman, P., Hartman, A., Srivastava, A., Molla, G., Madad, S., Fernandez-Granda, C., et al. (2024) · Nature Biotechnology · doi:10.1038/s41587-023-01767-y · Cited by 5,631 · 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

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 1,511 · 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

Using clusterProfiler to characterize multiomics data

Xu, S., Hu, E., Cai, Y., Xie, Z., Luo, X. M., Zhan, L., Tang, W., Wang, Q., Liu, B., Wang, R., et al. (2024) · Nature Protocols · doi:10.1038/s41596-024-01020-z · Cited by 1,487 · 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

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmia

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

EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data

Lun, A. T. L., Riesenfeld, S. J., Andrews, T. S., Dao, T. P., Gomes, T., Jamboree, P. I. T. 1. H. C. A., & Marioni, J. C. (2019) · Genome biology · doi:10.1186/s13059-019-1662-y · Cited by 1,044 · see citing works

Spatial transcriptomics at subspot resolution with BayesSpace

Zhao, E. J., Stone, M. R., Ren, X., Guenthoer, J., Smythe, K. S., Pulliam, T., Williams, S. R., Uytingco, C. R., Taylor, S. E. B., Nghiem, P., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00935-2 · Cited by 991 · 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

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

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

Single-Cell RNA-Seq Technologies and Related Computational Data Analysis

Chen, G., Ning, B., & Shi, T. (2019) · Frontiers in Genetics · doi:10.3389/fgene.2019.00317 · Cited by 913 · 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

edgeR v4: powerful differential analysis of sequencing data with expanded functionality and improved support for small counts and larger datasets

Chen, Y., Chen, L., Lun, A. T. L., Baldoni, P. L., & Smyth, G. K. (2025) · Nucleic Acids Research · doi:10.1093/nar/gkaf018 · Cited by 903 · 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

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

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

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

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

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

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

scmap: projection of single-cell RNA-seq data across data sets

(2018) · doi:10.1038/nmeth.4644 · Cited by 735 · 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

Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows

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

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

Molecular topography of an entire nervous system

Taylor, S. R., Santpere, G., Weinreb, A., Barrett, A., Reilly, M. B., Xu, C., Varol, E., Oikonomou, P., Glenwinkel, L., McWhirter, R. D., et al. (2021) · Cell · doi:10.1016/j.cell.2021.06.023 · Cited by 681 · 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

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

The dawn of spatial omics

Bressan, D., Battistoni, G., & Hannon, G. J. (2023) · Science · doi:10.1126/science.abq4964 · Cited by 606 · see citing works

Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell

Ayyaz, A., Kumar, S., Sangiorgi, B., Ghoshal, B., Gosio, J., Ouladan, S., Fink, M., Barutcu, S., Trcka, D., Shen, J., et al. (2019) · Nature · doi:10.1038/s41586-019-1154-y · Cited by 569 · 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

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

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

muscat detects subpopulation-specific state transitions from multi-sample multi-condition single-cell transcriptomics data

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

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

The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2020 update

Jalili, V., Afgan, E., Gu, Q., Clements, D., Blankenberg, D. J., Goecks, J., Taylor, J., & Nekrutenko, A. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa434 · Cited by 510 · see citing works

MYC Drives Temporal Evolution of Small Cell Lung Cancer Subtypes by Reprogramming Neuroendocrine Fate

Ireland, A. S., Micinski, A. M., Kastner, D. W., Guo, B., Wait, S. J., Spainhower, K. B., Conley, C. C., Chen, O. S., Guthrie, M. R., Soltero, D., et al. (2020) · Cancer Cell · doi:10.1016/j.ccell.2020.05.001 · Cited by 506 · see citing works

Progressive immune dysfunction with advancing disease stage in renal cell carcinoma

Braun, D. A., Street, K., Burke, K. P., Cookmeyer, D. L., Denize, T., Pedersen, C. B., Gohil, S. H., Schindler, N. R., Pomerance, L., Hirsch, L., et al. (2021) · Cancer Cell · doi:10.1016/j.ccell.2021.02.013 · Cited by 502 · 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

Benchmarking spatial and single-cell transcriptomics integration methods for transcript distribution prediction and cell type deconvolution

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

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

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

Human Pluripotent Stem Cell-Derived Neural Cells and Brain Organoids Reveal SARS-CoV-2 Neurotropism Predominates in Choroid Plexus Epithelium

Jacob, F., Pather, S. R., Huang, W., Zhang, F., Wong, S. Z. H., Zhou, H., Cubitt, B., Fan, W., Chen, C. Z., Xu, M., et al. (2020) · Cell stem cell · doi:10.1016/j.stem.2020.09.016 · Cited by 430 · 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 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

Spatiotemporal immune zonation of the human kidney

Stewart, B. J., Ferdinand, J. R., Young, M. D., Mitchell, T. J., Loudon, K. W., Riding, A., Richoz, N., Frazer, G. L., Staniforth, J., Braga, F. A. V., et al. (2019) · Science · doi:10.1126/science.aat5031 · Cited by 416 · see citing works

ReactomeGSA - Efficient Multi-Omics Comparative Pathway Analysis

Griss, J., Viteri, G., Sidiropoulos, K., Nguyen, V., Fabregat, A., & Hermjakob, H. (2020) · Molecular & Cellular Proteomics · doi:10.1074/mcp.tir120.002155 · Cited by 398 · see citing works

Cholangiocyte organoids can repair bile ducts after transplantation in the human liver

Sampaziotis, F., Muraro, D., Tysoe, O. C., Sawiak, S., Beach, T. E., Godfrey, E. M., Upponi, S. S., Brevini, T., Wesley, B. T., Garcia-Bernardo, J., et al. (2021) · Science · doi:10.1126/science.aaz6964 · Cited by 394 · see citing works

Distinct contributions of partial and full EMT to breast cancer malignancy

Lüönd, F., Sugiyama, N., Bill, R., Bornes, L., Hager, C., Tang, F., Santacroce, N., Beisel, C., Ivánek, R., Bürglin, T. R., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2021.11.006 · Cited by 390 · see citing works

Single-cell mapping of lineage and identity in direct reprogramming

Biddy, B. A., Kong, W., Kamimoto, K., Guo, C., Waye, S. E., Sun, T., & Morris, S. A. (2018) · Nature · doi:10.1038/s41586-018-0744-4 · Cited by 382 · see citing works

Plasticity of Epididymal Adipose Tissue in Response to Diet-Induced Obesity at Single-Nucleus Resolution

Sárvári, A. K., Hauwaert, E. L. V., Markussen, L. K., Gammelmark, E., Marcher, A.-B., Ebbesen, M. F., Nielsen, R., Brewer, J. R., Madsen, J. G. S., & Mandrup, S. (2020) · Cell Metabolism · doi:10.1016/j.cmet.2020.12.004 · Cited by 368 · 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

Macrophage and neutrophil heterogeneity at single-cell spatial resolution in human inflammatory bowel disease

Garrido-Trigo, A., Corraliza, A. M., Veny, M., Dotti, I., Melón-Ardanaz, E., Rill, A., Crowell, H. L., Corbí, A. L., Gudiño, V., Esteller, M., et al. (2023) · Nature Communications · doi:10.1038/s41467-023-40156-6 · Cited by 348 · see citing works

Peripheral CD8+ T cell characteristics associated with durable responses to immune checkpoint blockade in patients with metastatic melanoma

Fairfax, B. P., Taylor, C. A., Watson, R. A., Nassiri, I., Danielli, S., Fang, H., Mahé, E. A., Cooper, R. A., Woodcock, V. K., Traill, Z. C., et al. (2020) · Nature Medicine · doi:10.1038/s41591-019-0734-6 · Cited by 340 · see citing works

Single-cell RNA-sequencing of differentiating iPS cells reveals dynamic genetic effects on gene expression

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

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

Parenchymal border macrophages regulate the flow dynamics of the cerebrospinal fluid

Drieu, A., Du, S., Storck, S. E., Rustenhoven, J., Papadopoulos, Z., Dykstra, T., Zhong, F., Kim, K., Blackburn, S., Mamuladze, T., et al. (2022) · Nature · doi:10.1038/s41586-022-05397-3 · Cited by 325 · see citing works

GABA-Receptive Microglia Selectively Sculpt Developing Inhibitory Circuits

Favuzzi, E., Huang, S., Saldi, G.-A., Binan, L., Ibrahim, L. A., Fernández-Otero, M., Cao, Y., Zeine, A., Sefah, A., Zheng, K., et al. (2020) · Cell · doi:10.1016/j.cell.2021.06.018 · Cited by 321 · 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

Analysis of classical neutrophils and polymorphonuclear myeloid-derived suppressor cells in cancer patients and tumor-bearing mice

Veglia, F., Hashimoto, A., Dweep, H., Sanseviero, E., Leo, A. D., Tcyganov, E. N., Kossenkov, A. V., Mulligan, C. G., Nam, B. T., Masters, G. A., et al. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20201803 · Cited by 314 · see citing works

Single-cell RNA sequencing in cardiovascular development, disease and medicine

Paik, D. T., Cho, S., Tian, L., Chang, H. Y., & Wu, J. C. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-0359-y · Cited by 306 · 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

Exploring the single-cell RNA-seq analysis landscape with the scRNA-tools database

Zappia, L., Phipson, B., & Oshlack, A. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006245 · Cited by 304 · see citing works

The Malaria Cell Atlas: Single parasite transcriptomes across the complete Plasmodium life cycle

Howick, V. M., Russell, A. J. C., Andrews, T. S., Heaton, H., Reid, A. J., Natarajan, K. N., Butungi, H., Metcalf, T., Verzier, L. H., Rayner, J. C., et al. (2019) · Science · doi:10.1126/science.aaw2619 · Cited by 302 · see citing works

Novel dynamics of human mucociliary differentiation revealed by single-cell RNA sequencing of nasal epithelial cultures

Ruiz 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

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

Non-neuronal expression of SARS-CoV-2 entry genes in the olfactory system suggests mechanisms underlying COVID-19-associated anosmiaPreprint

Brann, D. H., Tsukahara, T., Weinreb, C., Lipovsek, M., den Berge, K. V., Gong, B., Chance, R., Macaulay, I. C., Chou, H.-J., Fletcher, R., et al. (2020) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2020.03.25.009084 · Cited by 294 · 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

LAG-3 and PD-1 synergize on CD8+ T cells to drive T cell exhaustion and hinder autocrine IFN-γ-dependent anti-tumor immunity

Andrews, L. P., Butler, S. C., Cui, J., Cillo, A. R., Cardello, C., Liu, C., Brunazzi, E. A., Baessler, A., Xie, B., Kunning, S. R., et al. (2024) · Cell · doi:10.1016/j.cell.2024.07.016 · Cited by 292 · see citing works

An end-to-end workflow for multiplexed image processing and analysis

Windhager, J., Zanotelli, V. R., Schulz, D., Meyer, L., Daniel, M., Bodenmiller, B., & Eling, N. (2023) · Nature Protocols · doi:10.1038/s41596-023-00881-0 · Cited by 290 · see citing works

Brain endothelial GSDMD activation mediates inflammatory BBB breakdown

Wei, C., Jiang, W., Wang, R., Zhong, H., He, H., Gao, X., Zhong, S., Yu, F., Guo, Q., Zhang, L., et al. (2024) · Nature · doi:10.1038/s41586-024-07314-2 · Cited by 288 · see citing works

Single-cell analysis of the cellular heterogeneity and interactions in the injured mouse spinal cord

Milich, L. M., Choi, J. S., Ryan, C. B., Cerqueira, S. R., Benavides, S., Yahn, S. L., Tsoulfas, P., & Lee, J. K. (2021) · The Journal of Experimental Medicine · doi:10.1084/jem.20210040 · Cited by 285 · see citing works

CHETAH: a selective, hierarchical cell type identification method for single-cell RNA sequencing

de Kanter, J. K., Lijnzaad, P., Candelli, T., Margaritis, T., & Holstege, F. C. P. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz543 · Cited by 274 · see citing works

Identification of direct connections between the dura and the brain

Smyth, L. C. D., Xu, D., Okar, S. V., Dykstra, T., Rustenhoven, J., Papadopoulos, Z., Bhasiin, K., Kim, M. W., Drieu, A., Mamuladze, T., et al. (2024) · Nature · doi:10.1038/s41586-023-06993-7 · Cited by 264 · see citing works

Single-nucleus RNA-seq identifies Huntington disease astrocyte states

Al-Dalahmah, O., Sosunov, A. A., Shaik, A., Ofori, K., Liu, Y., Vonsattel, J. P., Adorjan, I., Menon, V., & Goldman, J. E. (2020) · Acta Neuropathologica Communications · doi:10.1186/s40478-020-0880-6 · Cited by 264 · see citing works

Single-cell RNA sequencing of developing maize ears facilitates functional analysis and trait candidate gene discovery

Xu, X., Crow, M., Rice, B. R., Li, F., Harris, B. D., Liu, L., Demesa-Arévalo, E., Lu, Z., Wang, L., Fox, N., et al. (2021) · Developmental Cell · doi:10.1016/j.devcel.2020.12.015 · Cited by 260 · 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

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 &amp; Hepatology · doi:10.1038/s41575-020-0304-x · Cited by 253 · see citing works

Single-cell RNAseq reveals seven classes of colonic sensory neuron

Hockley, J. R. F., Taylor, T. S., Callejo, G., Wilbrey, A. L., Gutteridge, A., Bach, K., Winchester, W. J., Bulmer, D. C., McMurray, G., & Smith, E. S. J. (2018) · Gut · doi:10.1136/gutjnl-2017-315631 · Cited by 251 · see citing works

Differential IRF8 Requirement Defines Two Pathways of Dendritic Cell Development in Humans

Cytlak, U., Resteu, A., Pagan, S., Green, K., Milne, P., Maisuria, S., McDonald, D., Hulme, G., Filby, A. J., Carpenter, B., et al. (2019) · Immunity · doi:10.1016/j.immuni.2020.07.003 · Cited by 250 · see citing works

Multiplexed imaging mass cytometry of the chemokine milieus in melanoma characterizes features of the response to immunotherapy

Hoch, T., Schulz, D., Eling, N., Gómez, J. M. M., Levesque, M. P., & Bodenmiller, B. (2022) · Science Immunology · doi:10.1126/sciimmunol.abk1692 · Cited by 246 · see citing works

ShinyCell: simple and sharable visualization of single-cell gene expression data

Ouyang, J. F., Kamaraj, U. S., Cao, E. Y., & Rackham, O. J. L. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab209 · Cited by 245 · see citing works

Spatially aware dimension reduction for spatial transcriptomics

Shang, L., & Zhou, X. (2022) · Nature Communications · doi:10.1038/s41467-022-34879-1 · Cited by 242 · see citing works

The neuropeptide VIP confers anticipatory mucosal immunity by regulating ILC3 activity

Seillet, C., Luong, K., Tellier, J., Jacquelot, N., Shen, R. D., Hickey, P. F., Wimmer, V. C., Whitehead, L. W., Rogers, K. L., Smyth, G. K., et al. (2019) · Nature Immunology · doi:10.1038/s41590-019-0567-y · Cited by 235 · see citing works

Macrophages provide a transient muscle stem cell niche via NAMPT secretion

Ratnayake, D., Nguyen, P. D., Rossello, F. J., Wimmer, V. C., Tan, J. L., Galvis, L. A., Julier, Z., Wood, A. J., Boudier, T., Isiaku, A. I., et al. (2021) · Nature · doi:10.1038/s41586-021-03199-7 · Cited by 227 · see citing works

Single-cell analysis reveals congruence between kidney organoids and human fetal kidney

Combes, A. N., Phipson, B., Zappia, L., Lawlor, K. T., Er, P. X., Oshlack, A., & Little, M. H. (2019) · Genome Medicine · doi:10.1186/s13073-019-0615-0 · Cited by 226 · see citing works

A comprehensive single-cell map of T cell exhaustion-associated immune environments in human breast cancer

Tietscher, S., Wagner, J., Anzeneder, T., Langwieder, C., Rees, M., Sobottka, B., de Souza, N., & Bodenmiller, B. (2023) · Nature Communications · doi:10.1038/s41467-022-35238-w · Cited by 225 · 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

Single-nucleus transcriptome analysis reveals cell-type-specific molecular signatures across reward circuitry in the human brain

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

scruff: an R/Bioconductor package for preprocessing single-cell RNA-sequencing data

Wang, Z., Hu, J., Johnson, W. E., & Campbell, J. D. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2797-2 · Cited by 29 · see citing works

scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data

Tian, L., Su, S., Dong, X., Amann‐Zalcenstein, D., Biben, C., Seidi, A., Hilton, D. J., Naik, S. H., & Ritchie, M. E. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006361 · Cited by 115 · see citing works

popsicleR: A R Package for Pre-processing and Quality Control Analysis of Single Cell RNA-seq Data

Grandi, F., Caroli, J., Romano, O., Marchionni, M., Forcato, M., & Bicciato, S. (2022) · Journal of Molecular Biology · doi:10.1016/j.jmb.2022.167560 · Cited by 22 · see citing works

Preprocessing and analysis of single-cell RNA-sequencing data

Pekkarinen, M. (2018) · Tampere University Institutional Repository (Tampere University) · Cited by 1 · see citing works

Comprehensive generation, visualization, and reporting of quality control metrics for single-cell RNA sequencing data

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