Tutorial: guidelines for the experimental design of single-cell RNA sequencing studies

STAR: ultrafast universal RNA-seq aligner

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limma powers differential expression analyses for RNA-sequencing and microarray studies

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Untitled work

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

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HTSeq—a Python framework to work with high-throughput sequencing data

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Differential expression analysis for sequence count data

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Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation

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TopHat: discovering splice junctions with RNA-Seq

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Principal component analysis

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UMAP: Uniform Manifold Approximation and Projection

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Near-optimal probabilistic RNA-seq quantification

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Adjusting batch effects in microarray expression data using empirical Bayes methods

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A scaling normalization method for differential expression analysis of RNA-seq data

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Massively parallel digital transcriptional profiling of single cells

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

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UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

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Spatial reconstruction of single-cell gene expression data

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Scenic: single-cell regulatory network inference and clustering.

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Dimensionality reduction for visualizing single-cell data using UMAP

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Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation

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Reversed graph embedding resolves complex single-cell trajectories

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Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

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Full-length RNA-seq from single cells using Smart-seq2

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mRNA-Seq whole-transcriptome analysis of a single cell

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

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Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris

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

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Simultaneous epitope and transcriptome measurement in single cells

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Single-Cell Transcriptomic Analysis of Primary and Metastatic Tumor Ecosystems in Head and Neck Cancer

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Batch effects in single-cell RNA-sequencing data are corrected by matching mutual nearest neighbors

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Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes, and progenitors

Villani, A.-C., Satija, R., Reynolds, G., Sarkizova, S., Shekhar, K., Fletcher, J., Griesbeck, M., Butler, A., Zheng, S., Lazo, S., et al. (2017) · Science · doi:10.1126/science.aah4573 · Cited by 2,413 · see citing works

Smart-seq2 for sensitive full-length transcriptome profiling in single cells

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Single-cell chromatin accessibility reveals principles of regulatory variation

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The human cell atlas

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UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracyPreprint

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Single-Cell Map of Diverse Immune Phenotypes in the Breast Tumor Microenvironment

Azizi, E., Carr, A. J., Plitas, G., Cornish, A. E., Konopacki, C., Prabhakaran, S., Nainys, J., Wu, K., Kiseliovas, V., Setty, M., et al. (2018) · Cell · doi:10.1016/j.cell.2018.05.060 · Cited by 1,976 · see citing works

Single-cell mRNA quantification and differential analysis with Census

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

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Recovering Gene Interactions from Single-Cell Data Using Data Diffusion

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Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types

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A single-cell survey of the small intestinal epithelium

Haber, A. L., Biton, M., Rogel, N., Herbst, R. H., Shekhar, K., Smillie, C., Burgin, G., Delorey, T. M., Howitt, M. R., Katz, Y., et al. (2017) · Nature · doi:10.1038/nature24489 · Cited by 1,750 · see citing works

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

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A comparison of single-cell trajectory inference methods

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Full-length mRNA-Seq from single-cell levels of RNA and individual circulating tumor cells

Ramsköld, D., Luo, S., Wang, Y.-C., Li, R., Deng, Q., Faridani, O. R., Daniels, G. A., Khrebtukova, I., Loring, J. F., Laurent, L. C., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2282 · Cited by 1,609 · see citing works

Mapping the Mouse Cell Atlas by Microwell-Seq

Han, X., Wang, R., Zhou, Y., Fei, L., Sun, H., Lai, S., Saadatpour, A., Zhou, Z., Chen, H., Ye, F., et al. (2018) · Cell · doi:10.1016/j.cell.2018.02.001 · Cited by 1,597 · see citing works

Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes

Segerstolpe, Å., Palasantza, A., Eliasson, P., Andersson, E.-M., Andréasson, A.-C., Sun, X., Picelli, S., Sabirsh, A., Clausen, M., Bjursell, M. K., et al. (2016) · Cell Metabolism · doi:10.1016/j.cmet.2016.08.020 · Cited by 1,550 · see citing works

A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure

Baron, M., Veres, A., Wolock, S. L., Faust, A. L., Gaujoux, R., Vetere, A., Ryu, J. H., Wagner, B. K., Shen-Orr, S. S., Klein, A. M., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.08.011 · Cited by 1,508 · 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

The Technology and Biology of Single-Cell RNA Sequencing

Kolodziejczyk, A. A., Kim, J. K., Svensson, V., Marioni, J. C., & Teichmann, S. A. (2015) · Molecular Cell · doi:10.1016/j.molcel.2015.04.005 · Cited by 1,487 · see citing works

The Human Cell Atlas

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Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding

Rosenberg, A. B., Roco, C. M., Muscat, R. A., Kuchina, A., Sample, P., Yao, Z., Graybuck, L. T., Peeler, D. J., Mukherjee, S., Chen, W., et al. (2018) · Science · doi:10.1126/science.aam8999 · Cited by 1,422 · see citing works

Comprehensive single cell transcriptional profiling of a multicellular organism by combinatorial indexing

Cao, J., Packer, J. S., Ramani, V., Cusanovich, D. A., Huynh, C., Daza, R., Qiu, X., Lee, C., Furlan, S. N., Steemers, F. J., et al. (2017) · doi:10.1101/104844 · Cited by 1,402 · see citing works

Bayesian approach to single-cell differential expression analysis

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Reconstructing lineage hierarchies of the distal lung epithelium using single-cell RNA-seq

Treutlein, B., Brownfield, D. G., Wu, A. R., Neff, N. F., Mantalas, G. L., Espinoza, F. H., Desai, T. J., Krasnow, M. A., & Quake, S. R. (2014) · Nature · doi:10.1038/nature13173 · Cited by 1,342 · see citing works

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation

Kang, H. M., Subramaniam, M., Targ, S., Nguyen, M., Maliskova, L., McCarthy, E., Wan, E., Wong, S., Byrnes, L., Lanata, C. M., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4042 · Cited by 1,339 · cited 1× · see citing works

A Single-Cell Transcriptome Atlas of the Human Pancreas

Muraro, M. J., Dharmadhikari, G., Grün, D., Gröen, N., Dielen, T., Jansen, E., van Gurp, L., Engelse, M. A., Carlotti, F., de Koning, E. J. P., et al. (2016) · Cell Systems · doi:10.1016/j.cels.2016.09.002 · Cited by 1,288 · see citing works

Multiplex single-cell profiling of chromatin accessibility by combinatorial cellular indexing

Cusanovich, D. A., Daza, R., Adey, A., Pliner, H. A., Christiansen, L., Gunderson, K. L., Steemers, F. J., Trapnell, C., & Shendure, J. (2015) · Science · doi:10.1126/science.aab1601 · Cited by 1,248 · 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

CEL-seq: single-cell RNA-seq by multiplexed linear amplification

Hashimshony, T., Wagner, F., Sher, N., & Yanai, I. (2012) · Cell Reports · doi:10.1016/j.celrep.2012.08.003 · Cited by 1,231 · see citing works

Single-cell RNA-seq supports a developmental hierarchy in human oligodendroglioma

Tirosh, I., Venteicher, A. S., Hebert, C. M., Escalante, L. E., Patel, A. P., Yizhak, K., Fisher, J. M., Rodman, C., Mount, C., Filbin, M. G., et al. (2016) · Nature · doi:10.1038/nature20123 · Cited by 1,216 · see citing works

Quantitative single-cell RNA-seq with unique molecular identifiers

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Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics

Shekhar, K., Lapan, S. W., Whitney, I. E., Tran, N. M., Macosko, E. Z., Kowalczyk, M., Adiconis, X., Levin, J. Z., Nemesh, J., Goldman, M., et al. (2016) · Cell · doi:10.1016/j.cell.2016.07.054 · Cited by 1,176 · see citing works

CEL-seq2: sensitive highly-multiplexed single-cell RNA-Seq

Hashimshony, T., Senderovich, N., Avital, G., Klochendler, A., de Leeuw, Y., Anavy, L., Gennert, D., Li, S., Livak, K. J., Rozenblatt-Rosen, O., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-0938-8 · Cited by 1,125 · 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

Transcriptional Heterogeneity and Lineage Commitment in Myeloid Progenitors

Paul, F., Arkin, Y., Giladi, A., Jaitin, D. A., Kenigsberg, E., Keren-Shaul, H., Winter, D., Lara-Astiaso, D., Gury, M., Weiner, A., et al. (2015) · Cell · doi:10.1016/j.cell.2015.11.013 · Cited by 1,082 · see citing works

Single-cell sequencing reveals dissociation-induced gene expression in tissue subpopulations

van den Brink, S. C., Sage, F., Vértesy, Á., Spanjaard, B., Peterson-Maduro, J., Baron, C. S., Robin, C., & van Oudenaarden, A. (2017) · Nature Methods · doi:10.1038/nmeth.4437 · Cited by 1,072 · see citing works

Stromal gene expression defines poor-prognosis subtypes in colorectal cancer

Calon, A., Lonardo, E., Berenguer-Llergo, A., Espinet, E., Hernando-Momblona, X., Iglesias, M., Sevillano, M., Palomo-Ponce, S., Tauriello, D. V. F., Byrom, D., et al. (2015) · Nature Genetics · doi:10.1038/ng.3225 · Cited by 1,070 · see citing works

Massively parallel single-nucleus RNA-seq with DroNc-seq

Habib, N., Avraham-Davidi, I., Basu, A., Burks, T., Shekhar, K., Hofree, M., Choudhury, S. R., Aguet, F., Gelfand, E., Ardlie, K., et al. (2017) · Nature Methods · doi:10.1038/nmeth.4407 · Cited by 1,069 · see citing works

Single-cell spatial reconstruction reveals global division of labour in the mammalian liver

Halpern, K. B., Shenhav, R., Matcovitch-Natan, O., Tóth, B., Lemze, D., Golan, M., Massasa, E. E., Baydatch, S., Landen, S., Moor, A. E., et al. (2017) · Nature · doi:10.1038/nature21065 · Cited by 1,059 · 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

In situ sequencing for RNA analysis in preserved tissue and cells

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The Intestinal Stem Cell Signature Identifies Colorectal Cancer Stem Cells and Predicts Disease Relapse

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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

Seq-Well: portable, low-cost RNA sequencing of single cells at high throughput

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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

Tn5 transposase and tagmentation procedures for massively scaled sequencing projectsPreprint

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Bias, robustness and scalability in single-cell differential expression analysis

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G&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

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

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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

Single-cell barcoding and sequencing using droplet microfluidics

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Quantitative assessment of single-cell RNA-sequencing methods

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Classification of low quality cells from single-cell RNA-seq data

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