Computational approaches for interpreting sc RNA‐seq data

Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

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

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WGCNA: an R package for weighted correlation network analysis

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Hierarchical Grouping to Optimize an Objective Function

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LIII. On lines and planes of closest fit to systems of points in space

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Adjusting batch effects in microarray expression data using empirical Bayes methods

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

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Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.

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The <tt>sva</tt> package for removing batch effects and other unwanted variation in high-throughput experiments

Leek, J. T., Johnson, W. E., Parker, H. S., Jaffe, A. E., & Storey, J. D. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts034 · Cited by 6,231 · see citing works

Reversed graph embedding resolves complex single-cell trajectories

Qiu, X., Mao, Q., Tang, Y., Wang, L., Chawla, R., Pliner, H. A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4402 · Cited by 5,070 · 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

Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells

Klein, A. M., Mazutis, L., Akartuna, I., Tallapragada, N., Veres, A., Li, V., Peshkin, L., Weitz, D. A., & Kirschner, M. W. (2015) · Cell · doi:10.1016/j.cell.2015.04.044 · Cited by 3,356 · 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

Single-cell chromatin accessibility reveals principles of regulatory variation

Buenrostro, J. D., Wu, B., Litzenburger, U. M., Ruff, D., Gonzales, M. L., Snyder, M. P., Chang, H. Y., & Greenleaf, W. J. (2015) · Nature · doi:10.1038/nature14590 · Cited by 2,308 · see citing works

The interpretation of interaction in contingency tables

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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

Imaging individual mRNA molecules using multiple singly labeled probes

Raj, A., van den Bogaard, P., Rifkin, S. A., van Oudenaarden, A., & Tyagi, S. (2008) · Nature Methods · doi:10.1038/nmeth.1253 · Cited by 2,028 · see citing works

Single-cell mRNA quantification and differential analysis with Census

Qiu, X., Hill, A. J., Packer, J. S., Lin, D., Ma, Y.-A., & Trapnell, C. (2017) · Nature Methods · doi:10.1038/nmeth.4150 · Cited by 1,885 · see citing works

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

McCarthy, D. J., Campbell, K. R., Lun, A. T. L., & Wills, Q. F. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btw777 · Cited by 1,852 · 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

Comparative Analysis of Single-Cell RNA Sequencing Methods

Ziegenhain, C., Vieth, B., Parekh, S., Reinius, B., Guillaumet-Adkins, A., Smets, M., Leonhardt, H., Heyn, H., Hellmann, I., & Enard, W. (2017) · Molecular Cell · doi:10.1016/j.molcel.2017.01.023 · Cited by 1,494 · see citing works

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

Single-Cell RNA-Seq Reveals Dynamic, Random Monoallelic Gene Expression in Mammalian Cells

Deng, Q., Ramsköld, D., Reinius, B., & Sandberg, R. (2014) · Science · doi:10.1126/science.1245316 · Cited by 1,345 · 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

Quantitative single-cell RNA-seq with unique molecular identifiers

Islam, S., Zeisel, A., Joost, S., La Manno, G., Zajac, P., Kasper, M., Lönnerberg, P., & Linnarsson, S. (2014) · Nature Methods · doi:10.1038/nmeth.2772 · Cited by 1,183 · see citing works

Computational and analytical challenges in single-cell transcriptomics

Stegle, O., Teichmann, S. A., & Marioni, J. C. (2015) · Nature Reviews Genetics · doi:10.1038/nrg3833 · Cited by 1,170 · 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

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Single-cell genome-wide bisulfite sequencing for assessing epigenetic heterogeneity

Smallwood, S. A., Lee, H. J., Angermueller, C., Krueger, F., Saadeh, H., Peat, J., Andrews, S. R., Stegle, O., Reik, W., & Kelsey, G. (2014) · Nature Methods · doi:10.1038/nmeth.3035 · Cited by 1,125 · 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

Genetic programs in human and mouse early embryos revealed by single-cell RNA sequencing

Xue, Z., Huang, K., Cai, C., Cai, L., Jiang, C.-Y., Feng, Y., Liu, Z., Zeng, Q., Cheng, L., Sun, Y. E., et al. (2013) · Nature · doi:10.1038/nature12364 · Cited by 963 · see citing works

Comparative Analysis of Single-Cell RNA Sequencing MethodsPreprint

Ziegenhain, C., Vieth, B., Parekh, S., et al. (2016) · bioRxiv · doi:10.1101/035758 · Cited by 951 · 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

G&amp;T-seq: parallel sequencing of single-cell genomes and transcriptomes

Macaulay, I. C., Haerty, W., Kumar, P., Li, Y. I., Hu, T. X., Teng, M. J., Goolam, M., Saurat, N., Coupland, P., Shirley, L. M., et al. (2015) · Nature Methods · doi:10.1038/nmeth.3370 · Cited by 750 · see citing works

Validation of noise models for single-cell transcriptomics

Grün, D., Kester, L., & van Oudenaarden, A. (2014) · Nature Methods · doi:10.1038/nmeth.2930 · Cited by 721 · see citing works

Parallel single-cell sequencing links transcriptional and epigenetic heterogeneity

Angermueller, C., Clark, S. J., Lee, H. J., Macaulay, I. C., Teng, M. J., Hu, T. X., Krueger, F., Smallwood, S. A., Ponting, C. P., Voet, T., et al. (2016) · Nature Methods · doi:10.1038/nmeth.3728 · Cited by 717 · see citing works

Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning

Wang, B., Zhu, J., Pierson, E., Ramazzotti, D., & Batzoglou, S. (2017) · Nature Methods · doi:10.1038/nmeth.4207 · Cited by 706 · 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

<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

Diffusion maps and coarse-graining: a unified framework for dimensionality reduction, graph partitioning, and data set parameterization

Lafon, S., & Lee, A. B. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.184 · Cited by 620 · see citing works

ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis

Pierson, E., & Yau, C. (2015) · Genome Biology · doi:10.1186/s13059-015-0805-z · Cited by 577 · see citing works

Power analysis of single-cell RNA-sequencing experiments

Svensson, V., Natarajan, K. N., Ly, L.-H., Miragaia, R. J., Labalette, C., Macaulay, I. C., Cvejic, A., & Teichmann, S. A. (2017) · Nature Methods · doi:10.1038/nmeth.4220 · Cited by 568 · see citing works

Identification of cell types from single-cell transcriptomes using a novel clustering method

Xu, C., & Su, Z. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv088 · Cited by 559 · see citing works

Wishbone identifies bifurcating developmental trajectories from single-cell data

Setty, M., Tadmor, M. D., Reich-Zeliger, S., Angel, O., Salame, T. M., Kathail, P., Choi, K., Bendall, S., Friedman, N., & Pe'er, D. (2016) · Nature Biotechnology · doi:10.1038/nbt.3569 · Cited by 558 · 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

Integrated genome and transcriptome sequencing of the same cell

Dey, S. S., Kester, L., Spanjaard, B., Bienko, M., & van Oudenaarden, A. (2015) · Nature Biotechnology · doi:10.1038/nbt.3129 · Cited by 502 · see citing works

Tracing the Derivation of Embryonic Stem Cells from the Inner Cell Mass by Single-Cell RNA-Seq Analysis

Tang, F., Bárbácioru, C., Bao, S., Lee, C. G. L., Nordman, E., Wang, X., Lao, K., & Surani, M. A. (2010) · Cell stem cell · doi:10.1016/j.stem.2010.03.015 · Cited by 499 · see citing works

From single-cell to cell-pool transcriptomes: Stochasticity in gene expression and RNA splicing

Marinov, G. K., Williams, B. A., McCue, K., Schroth, G. P., Gertz, J., Myers, R. M., & Wold, B. J. (2014) · Genome Research · doi:10.1101/gr.161034.113 · Cited by 468 · see citing works

NOTES ON THE THEORY OF ASSOCIATION OF ATTRIBUTES IN STATISTICS

Yule, G. U. (1903) · Biometrika · doi:10.1093/biomet/2.2.121 · Cited by 466 · see citing works

Adjustment of systematic microarray data biases

Benito, M., Parker, J., Du, Q., Wu, J., Xiang, D., Perou, C. M., & Marron, J. S. (2004) · Bioinformatics · doi:10.1093/bioinformatics/btg385 · Cited by 426 · see citing works

Kraken: A set of tools for quality control and analysis of high-throughput sequence data

Davis, M. P. A., van Dongen, S., Abreu‐Goodger, C., Bartoniček, N., & Enright, A. J. (2013) · Methods · doi:10.1016/j.ymeth.2013.06.027 · Cited by 418 · see citing works

Decoding the regulatory network of early blood development from single-cell gene expression measurements

Moignard, V., Woodhouse, S., Haghverdi, L., Lilly, A. J., Tanaka, Y., Wilkinson, A. C., Buettner, F., Macaulay, I. C., Jawaid, W., Diamanti, E., et al. (2015) · Nature Biotechnology · doi:10.1038/nbt.3154 · Cited by 380 · see citing works

The first five years of single-cell cancer genomics and beyondPreprint

Navin, N. E. (2015) · Genome Research · doi:10.1101/gr.191098.115 · Cited by 368 · 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

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

Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape

Marco, E., Karp, R. L., Guo, G., Robson, P., Hart, A. H., Trippa, L., & Yuan, G.-C. (2014) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1408993111 · Cited by 268 · see citing works

Single-cell RNA-seq and computational analysis using temporal mixture modeling resolves T H 1/T FH fate bifurcation in malaria

Lönnberg, T., Svensson, V., James, K. R., Fernandez‐Ruiz, D., Sebina, I., Montandon, R., Soon, M. S. F., Fogg, L. G., Nair, A. S., Liligeto, U. N., et al. (2017) · Science Immunology · doi:10.1126/sciimmunol.aal2192 · Cited by 260 · 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

Single-Cell RNA Sequencing Reveals T Helper Cells Synthesizing Steroids De Novo to Contribute to Immune Homeostasis

Mahata, B., Zhang, X., Kolodziejczyk, A. A., Proserpio, V., Haim-Vilmovsky, L., TAYLOR, A. E. R., Hebenstreit, D., Dingler, F. A., Moignard, V., Göttgens, B., et al. (2014) · Cell Reports · doi:10.1016/j.celrep.2014.04.011 · Cited by 212 · see citing works

Inferring the kinetics of stochastic gene expression from single-cell RNA-sequencing data

Kim, J. K., & Marioni, J. C. (2013) · Genome biology · doi:10.1186/gb-2013-14-1-r7 · Cited by 207 · see citing works

Di↵erential analysis of count data - the DESeq2 package

Love, M. I., Anders, S., & Huber, W. (2014) · Cited by 202 · see citing works

Single-Cell RNA-Sequencing Reveals a Continuous Spectrum of Differentiation in Hematopoietic Cells

Macaulay, I. C., Svensson, V., Labalette, C., Ferreira, L., Hamey, F., Voet, T., Teichmann, S. A., & Cvejic, A. (2016) · Cell Reports · doi:10.1016/j.celrep.2015.12.082 · Cited by 191 · see citing works

Modeling of RNA-seq fragment sequence bias reduces systematic errors in transcript abundance estimation

Love, M. I., Hogenesch, J. B., & Irizarry, R. A. (2016) · Nature Biotechnology · doi:10.1038/nbt.3682 · Cited by 186 · see citing works

Analysis of allelic expression patterns in clonal somatic cells by single-cell RNA–seq

Reinius, B., Mold, J. E., Ramsköld, D., Deng, Q., Johnsson, P., Michaëlsson, J., Frisén, J., & Sandberg, R. (2016) · Nature Genetics · doi:10.1038/ng.3678 · Cited by 149 · see citing works

Reconstructing the temporal ordering of biological samples using microarray data

Magwene, P. M., Lizardi, P. M., & Kim, J. (2003) · Bioinformatics · doi:10.1093/bioinformatics/btg081 · Cited by 108 · see citing works

Dimensionality Reduction Via Graph Structure Learning

Mao, Q., Wang, L., Steve Goodison, B., & Sun, Y. (2015) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2783258.2783309 · Cited by 104 · see citing works

A Simple Approach to Ranking Differentially Expressed Gene Expression Time Courses through Gaussian Process Regression

Kalaitzis, A. A., & Lawrence, N. D. (2011) · BMC Bioinformatics · doi:10.1186/1471-2105-12-180 · Cited by 91 · see citing works

Reversed graph embedding resolves complex single-cell developmental trajectoriesPreprint

Qiu, X., Mao, Q., Tang, Y., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/110668 · Cited by 90 · see citing works

Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expression

Kar, G., Kim, J. K., Kolodziejczyk, A. A., Natarajan, K. N., Triglia, E. T., Mifsud, B., Elderkin, S., Marioni, J. C., Pombo, A., & Teichmann, S. A. (2017) · Nature Communications · doi:10.1038/s41467-017-00052-2 · Cited by 75 · see citing works

Mpath maps multi-branching single-cell trajectories revealing progenitor cell progression during development

Chen, J., Schlitzer, A., Chakarov, S., Ginhoux, F., & Poidinger, M. (2016) · Nature Communications · doi:10.1038/ncomms11988 · Cited by 72 · see citing works

Order Under Uncertainty: Robust Differential Expression Analysis Using Probabilistic Models for Pseudotime Inference

Campbell, K. R., & Yau, C. (2016) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005212 · Cited by 66 · see citing works

Single‐cell technologies to study the immune system

Proserpio, V., & Mahata, B. (2015) · Immunology · doi:10.1111/imm.12553 · Cited by 61 · see citing works

Robust detection of alternative splicing in a population of single cells

Welch, J. D., Hu, Y., & Prins, J. F. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1525 · Cited by 57 · see citing works

scater: pre-processing, quality control, normalisation and visualisation of single-cell RNA-seq data in RPreprint

McCarthy, D. J., Campbell, K. R., Lun, A. T. L., et al. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/069633 · Cited by 46 · see citing works

Single-Neuron Transcriptome and Methylome Sequencing for Epigenomic Analysis of Aging

Moroz, L. L., & Kohn, A. B. (2013) · Methods in Molecular Biology · doi:10.1007/978-1-62703-556-9_21 · Cited by 42 · see citing works

switchde: inference of switch-like differential expression along single-cell trajectories

Campbell, K. R., & Yau, C. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw798 · Cited by 36 · see citing works

ImpulseDE: detection of differentially expressed genes in time series data using impulse models

Sander, J., Schultze, J. L., & Yosef, N. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw665 · Cited by 35 · see citing works

Genetics and immunity in the era of single-cell genomics

Braga, F. A. V., Teichmann, S. A., & Chen, X. X. (2016) · Human Molecular Genetics · doi:10.1093/hmg/ddw192 · Cited by 20 · see citing works

OEFinder: a user interface to identify and visualize ordering effects in single-cell RNA-seq data

Leng, N., Choi, J., Chu, L., Thomson, J. A., Kendziorski, C. M., & Stewart, R. M. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw004 · Cited by 15 · 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

Single-cell RNA-seq to decipher tumour architecture

Cloney, R. (2016) · Nature Reviews Genetics · doi:10.1038/nrg.2016.151 · Cited by 9 · see citing works

Isolator: accurate and stable analysis of isoform-level expression in RNA-Seq experimentsPreprint

Jones, D. C., Kuppusamy, K. T., Palpant, N. J., Peng, X., Murry, C. E., Ruohola‐Baker, H., & Ruzzo, W. L. (2016) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/088765 · Cited by 8 · see citing works

Flipping between Polycomb repressed and active transcriptional states introduces noise in gene expressionPreprint

Kar, G., Kim, J. K., Kolodziejczyk, A. A., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/117267 · Cited by 5 · see citing works

High expression of ACE2 receptor of 2019-nCoV on the epithelial cells of oral mucosa

Xu, H., Zhong, L., Deng, J., Peng, J., Dan, H., Zeng, X., Li, T., & Chen, Q. (2020) · International Journal of Oral Science · doi:10.1038/s41368-020-0074-x · Cited by 2,838 · see citing works

Current best practices in single-cell RNA-seq analysis: a tutorial

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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

Single-cell transcriptomics to explore the immune system in health and disease

Stubbington, M. J. T., Rozenblatt–Rosen, O., Regev, A., & Teichmann, S. A. (2017) · Science · doi:10.1126/science.aan6828 · Cited by 560 · 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

Single-Cell Sequencing of Brain Cell Transcriptomes and Epigenomes

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Single-Cell RNA-Seq Reveals Dynamic Change in Tumor Microenvironment During Pancreatic Ductal Adenocarcinoma Malignant Progression

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Phenotyping of immune and endometrial epithelial cells in endometrial carcinomas revealed by single-cell RNA sequencing

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The Unmixing Problem: A Guide to Applying Single-Cell RNA Sequencing to Bone

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An optimized FACS-free single-nucleus RNA sequencing (snRNA-seq) method for plant science research

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A Bayesian mixture model for the analysis of allelic expression in single cells

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Characterization of hormone-producing cell types in the teleost pituitary gland using single-cell RNA-seq

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Single-Cell Sequencing in Precision Medicine

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A Primer for Single-Cell Sequencing in Non-Model Organisms

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Single-Cell Transcriptomics Uncovers Cellular Heterogeneity, Mechanisms, and Therapeutic Targets for Parkinson’s Disease

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Subpopulation Detection and Their Comparative Analysis across Single-Cell Experiments with scPopCorn

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Single-Cell RNA Sequencing Data Clustering by Low-Rank Subspace Ensemble Framework

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Single‐Cell Sequencing in Normal and Malignant Hematopoiesis

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Single-Cell RNA Sequencing of Ovarian Cancer: Promises and Challenges

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Single-cell RNA-sequencing data analysis reveals a highly correlated triphasic transcriptional response to SARS-CoV-2 infection

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scASK: A Novel Ensemble Framework for Classifying Cell Types Based on Single-cell RNA-seq Data

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Differential Network Analysis Reveals Regulatory Patterns in Neural Stem Cell Fate Decision

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Enhancing Clustering of Single-Cell RNA-Seq Data by Proximity Learning on Random Projected Spaces

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