1 Workflow version information

R version: R version 3.6.0 (2019-04-26)

Bioconductor version: 3.9

Package: 1.8.0

2 Motivation

Single-cell RNA sequencing (scRNA-seq) is widely used to measure the genome-wide expression profile of individual cells. From each cell, mRNA is isolated and reverse transcribed to cDNA for high-throughput sequencing (Stegle, Teichmann, and Marioni 2015). This can be done using microfluidics platforms like the Fluidigm C1 (Pollen et al. 2014), protocols based on microtiter plates like Smart-seq2 (Picelli et al. 2014), or droplet-based technologies like inDrop (Klein et al. 2015; Macosko et al. 2015). The number of reads mapped to each gene is then used to quantify its expression in each cell. Alternatively, unique molecular identifiers (UMIs) can be used to directly measure the number of transcript molecules for each gene (Islam et al. 2014). Count data are analyzed to detect highly variable genes (HVGs) that drive heterogeneity across cells in a population, to find correlations between genes and cellular phenotypes, or to identify new subpopulations via dimensionality reduction and clustering. This provides biological insights at a single-cell resolution that cannot be achieved with conventional bulk RNA sequencing of cell populations.

Strategies for scRNA-seq data analysis differ markedly from those for bulk RNA-seq. One technical reason is that scRNA-seq data are much noisier than bulk data (Brennecke et al. 2013; Marinov et al. 2014). Reliable capture (i.e., conversion) of transcripts into cDNA for sequencing is difficult with the low quantity of RNA in a single cell. This increases the frequency of drop-out events where none of the transcripts for a gene are captured. Dedicated steps are required to deal with this noise during analysis, especially during quality control. In addition, scRNA-seq data can be used to study cell-to-cell heterogeneity, e.g., to identify new cell subtypes, to characterize differentiation processes, to assign cells into their cell cycle phases, or to identify HVGs driving variability across the population (Vallejos, Marioni, and Richardson 2015; Fan et al. 2016; Trapnell et al. 2014). This is simply not possible with bulk data, meaning that custom methods are required to perform these analyses.

3 scRNA-seq data analysis with Bioconductor

This package contains a set of computational workflows for basic analysis of scRNA-seq data, using software from the open-source Bioconductor project (Huber et al. 2015). The workflows start from a count matrix and describe a number of key steps for scRNA-seq data analysis, including:

  • quality control to remove problematic cells;
  • normalization of cell-specific biases, with and without spike-ins;
  • correction for batch effects;
  • cell cycle phase classification from gene expression data;
  • data exploration to identify putative subpopulations;
  • and finally, HVG and marker gene identification to prioritize interesting genes.

The application of these procedures will be demonstrated on several public scRNA-seq datasets involving immortalized myeloid progenitors, brain cells, haematopoietic stem cells, T-helper cells and mouse embryonic stem cells, generated with a range of experimental protocols and platforms (Lun et al. 2017; Wilson et al. 2015; Zeisel et al. 2015; Islam et al. 2011; Buettner et al. 2015; Zheng et al. 2017). The aim is to provide a variety of modular usage examples that can be applied by readers to construct custom analysis pipelines for their own experiments.

See the simpleSingleCell landing page for links to individual workflows and for instructions on how to install the required packages. To cite any of these workflows, please refer to http://f1000research.com/articles/5-2122/v2 for instructions.

4 Obtaining a count matrix

All of these workflows start from a publicly available count matrix. For simplicity, we forego a description of the read processing steps required to generate the count matrix, i.e., read alignment and counting into features. For SMART-seq2 data (Picelli et al. 2014), quantification procedures developed for bulk RNA-seq are generally satisfactory (Love et al. 2015; Chen, Lun, and Smyth 2016). Users favouring an R-based approach to read alignment and counting might consider using the methods in the Rsubread package (Liao, Smyth, and Shi 2013, 2014).

Many other scRNA-seq protocols contain bespoke sequence structures that require careful processing:

  • Unique molecular identifiers (UMIs) (Islam et al. 2014) are widely used to mitigate the effects of amplification biases. Reads with the same UMI mapping to the same gene represent a single underlying transcript molecule and only increment the count of that gene by one. Processing of this data requires extraction of the UMI sequence from each read or read pair, and a method to collapse UMI-based duplicates into a single count (Smith, Heger, and Sudbery 2017).
  • Protocols may also use custom cell barcodes to improve multiplexing efficiency beyond that offered by the standard sequencing barcodes (e.g., from Illumina). This includes data generated from droplet-based experiments (Zheng et al. 2017) or from very high-throughput plate-based protocols like MARS-seq (Jaitin et al. 2014). Processing of this data usually requires a separate step to extract the barcode sequence from each read and to allocate the read to the correct per-cell sequencing library.

For these data sets, the scPipe package (Tian et al. 2018) provides an R-based processing pipeline for obtaining a count matrix.

If spike-in RNA was added, the sequences of the spike-in transcripts can be included as additional FASTA files during genome index building prior to alignment. Similarly, genomic intervals for both spike-in transcripts and endogenous genes can be concatenated into a single GTF file prior to counting.

5 Author information

5.1 Author contributions

A.T.L.L. developed and tested workflows on all datasets. A.T.L.L. and D.J.M. implemented improvements to the software packages required by the workflow. J.C.M. provided direction to the software and workflow development. All authors wrote and approved the final manuscript.

5.2 Competing interests

No competing interests were disclosed.

5.3 Grant information

A.T.L.L. and J.C.M. were supported by core funding from Cancer Research UK (award no. A17197). D.J.M. was supported by a CJ Martin Fellowship from the National Health and Medical Research Council of Australia. D.J.M and J.C.M. were also supported by core funding from EMBL.

5.4 Acknowledgements

We would like to thank Antonio Scialdone for helpful discussions, as well as Michael Epstein, James R. Smith and John Wilson-Kanamori for testing the workflow on other datasets.


Brennecke, P., S. Anders, J. K. Kim, A. A. Kołodziejczyk, X. Zhang, V. Proserpio, B. Baying, et al. 2013. “Accounting for technical noise in single-cell RNA-seq experiments.” Nat. Methods 10 (11):1093–5.

Buettner, F., K. N. Natarajan, F. P. Casale, V. Proserpio, A. Scialdone, F. J. Theis, S. A. Teichmann, J. C. Marioni, and O. Stegle. 2015. “Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.” Nat. Biotechnol. 33 (2):155–60.

Chen, Y., A. T. Lun, and G. K. Smyth. 2016. “From reads to genes to pathways: differential expression analysis of RNA-Seq experiments using Rsubread and the edgeR quasi-likelihood pipeline.” F1000Res 5:1438.

Fan, J., N. Salathia, R. Liu, G. E. Kaeser, Y. C. Yung, J. L. Herman, F. Kaper, et al. 2016. “Characterizing transcriptional heterogeneity through pathway and gene set overdispersion analysis.” Nat. Methods 13 (3):241–44.

Huber, W., V. J. Carey, R. Gentleman, S. Anders, M. Carlson, B. S. Carvalho, H. C. Bravo, et al. 2015. “Orchestrating high-throughput genomic analysis with Bioconductor.” Nat. Methods 12 (2):115–21.

Islam, S., U. Kjallquist, A. Moliner, P. Zajac, J. B. Fan, P. Lonnerberg, and S. Linnarsson. 2011. “Characterization of the single-cell transcriptional landscape by highly multiplex RNA-seq.” Genome Res. 21 (7):1160–7.

Islam, S., A. Zeisel, S. Joost, G. La Manno, P. Zajac, M. Kasper, P. Lonnerberg, and S. Linnarsson. 2014. “Quantitative single-cell RNA-seq with unique molecular identifiers.” Nat. Methods 11 (2):163–66.

Jaitin, D. A., E. Kenigsberg, H. Keren-Shaul, N. Elefant, F. Paul, I. Zaretsky, A. Mildner, et al. 2014. “Massively parallel single-cell RNA-seq for marker-free decomposition of tissues into cell types.” Science 343 (6172):776–79.

Klein, A. M., L. Mazutis, I. Akartuna, N. Tallapragada, A. Veres, V. Li, L. Peshkin, D. A. Weitz, and M. W. Kirschner. 2015. “Droplet barcoding for single-cell transcriptomics applied to embryonic stem cells.” Cell 161 (5):1187–1201.

Liao, Y., G. K. Smyth, and W. Shi. 2013. “The Subread aligner: fast, accurate and scalable read mapping by seed-and-vote.” Nucleic Acids Res. 41 (10):e108.

———. 2014. “featureCounts: an efficient general purpose program for assigning sequence reads to genomic features.” Bioinformatics 30 (7):923–30.

Love, M. I., S. Anders, V. Kim, and W. Huber. 2015. “RNA-Seq workflow: gene-level exploratory analysis and differential expression.” F1000Res 4:1070.

Lun, A. T. L., F. J. Calero-Nieto, L. Haim-Vilmovsky, B. Gottgens, and J. C. Marioni. 2017. “Assessing the reliability of spike-in normalization for analyses of single-cell RNA sequencing data.” Genome Res. 27 (11):1795–1806.

Macosko, E. Z., A. Basu, R. Satija, J. Nemesh, K. Shekhar, M. Goldman, I. Tirosh, et al. 2015. “Highly parallel genome-wide expression profiling of individual cells using nanoliter droplets.” Cell 161 (5):1202–14.

Marinov, G. K., B. A. Williams, K. McCue, G. P. Schroth, J. Gertz, R. M. Myers, and B. J. Wold. 2014. “From single-cell to cell-pool transcriptomes: stochasticity in gene expression and RNA splicing.” Genome Res. 24 (3):496–510.

Picelli, S., O. R. Faridani, A. K. Bjorklund, G. Winberg, S. Sagasser, and R. Sandberg. 2014. “Full-length RNA-seq from single cells using Smart-seq2.” Nat Protoc 9 (1):171–81.

Pollen, A. A., T. J. Nowakowski, J. Shuga, X. Wang, A. A. Leyrat, J. H. Lui, N. Li, et al. 2014. “Low-coverage single-cell mRNA sequencing reveals cellular heterogeneity and activated signaling pathways in developing cerebral cortex.” Nat. Biotechnol. 32 (10):1053–8.

Smith, T., A. Heger, and I. Sudbery. 2017. “UMI-tools: modeling sequencing errors in Unique Molecular Identifiers to improve quantification accuracy.” Genome Res. 27 (3):491–99.

Stegle, O., S. A. Teichmann, and J. C. Marioni. 2015. “Computational and analytical challenges in single-cell transcriptomics.” Nat. Rev. Genet. 16 (3):133–45.

Tian, L., S. Su, X. Dong, D. Amann-Zalcenstein, C. Biben, A. Seidi, D. J. Hilton, S. H. Naik, and M. E. Ritchie. 2018. “scPipe: A flexible R/Bioconductor preprocessing pipeline for single-cell RNA-sequencing data.” PLoS Comput. Biol. 14 (8):e1006361.

Trapnell, C., D. Cacchiarelli, J. Grimsby, P. Pokharel, S. Li, M. Morse, N. J. Lennon, K. J. Livak, T. S. Mikkelsen, and J. L. Rinn. 2014. “The dynamics and regulators of cell fate decisions are revealed by pseudotemporal ordering of single cells.” Nat. Biotechnol. 32 (4):381–86.

Vallejos, C. A., J. C. Marioni, and S. Richardson. 2015. “BASiCS: Bayesian analysis of single-cell sequencing data.” PLoS Comput. Biol. 11 (6):e1004333.

Wilson, N. K., D. G. Kent, F. Buettner, M. Shehata, I. C. Macaulay, F. J. Calero-Nieto, M. Sanchez Castillo, et al. 2015. “Combined single-cell functional and gene expression analysis resolves heterogeneity within stem cell populations.” Cell Stem Cell 16 (6):712–24.

Zeisel, A., A. B. Munoz-Manchado, S. Codeluppi, P. Lonnerberg, G. La Manno, A. Jureus, S. Marques, et al. 2015. “Brain structure. Cell types in the mouse cortex and hippocampus revealed by single-cell RNA-seq.” Science 347 (6226):1138–42.

Zheng, G. X., J. M. Terry, P. Belgrader, P. Ryvkin, Z. W. Bent, R. Wilson, S. B. Ziraldo, et al. 2017. “Massively parallel digital transcriptional profiling of single cells.” Nat Commun 8 (January):14049.