Contents

1 Introduction

The scRNAseq package provides convenient access to several publicly available data sets in the form of SingleCellExperiment objects. The focus of this package is to capture datasets that are not easily read into R with a one-liner from, e.g., read.csv(). Instead, we do the necessary data munging so that users only need to call a single function to obtain a well-formed SingleCellExperiment. For example:

library(scRNAseq)
fluidigm <- ReprocessedFluidigmData()
fluidigm
## class: SingleCellExperiment 
## dim: 26255 130 
## metadata(3): sample_info clusters which_qc
## assays(4): tophat_counts cufflinks_fpkm rsem_counts rsem_tpm
## rownames(26255): A1BG A1BG-AS1 ... ZZEF1 ZZZ3
## rowData names(0):
## colnames(130): SRR1275356 SRR1274090 ... SRR1275366 SRR1275261
## colData names(28): NREADS NALIGNED ... Cluster1 Cluster2
## reducedDimNames(0):
## spikeNames(0):
## altExpNames(0):

Readers are referred to the SummarizedExperiment and SingleCellExperiment documentation for further information on how to work with SingleCellExperiment objects.

2 Available data sets

The available data sets can be split into two categories. The first category contains expression matrices that have been generated by the scRNAseq authors from the raw sequencing data for each experiment. This includes:

The second category contains expression matrices that were provided by the authors of each study. No further reprocessing has been performed other than some cross-checks betweeh the count matrix and the sample metadata.

Study System Number of cells Function
Baron et al. (2016) Human pancreas 8569 BaronPancreasData('human')
Baron et al. (2016) Mouse pancreas 1886 BaronPancreasData('mouse')
Campbell et al. (2017) Mouse brain 21086 CampbellBrainData()
Chen et al. (2017) Mouse brain 14437 ChenBrainData()
Grun et al. (2016) Mouse haematopoietic stem cells 1915 GrunHSCData()
Grun et al. (2016) Human pancreas 1728 GrunPancreasData()
La Manno et al. (2016) Human embryonic stem cells 1715 LaMannoBrainData('human-es')
La Manno et al. (2016) Human embryonic midbrain 1977 LaMannoBrainData('human-embryo')
La Manno et al. (2016) Human induced pluripotent stem cells 337 LaMannoBrainData('human-ips')
La Manno et al. (2016) Mouse adult dopaminergic neurons 243 LaMannoBrainData('mouse-adult')
La Manno et al. (2016) Human embyronic midbrain 1907 LaMannoBrainData('mouse-embryo')
Lawlor et al. (2017) Human pancreas 638 LawlorPancreasData()
Lun et al. (2017) 416B cells 192 LunSpikeInData('416b')
Lun et al. (2017) Mouse trophoblasts 192 LunSpikeInData('tropho')
Macosko et al. (2015) Mouse retina 49300 MacoskoRetinaData()
Marques et al. (2016) Mouse brain 5069 MarquesBrainData()
Muraro et al. (2016) Human pancreas 3072 MuraroPancreasData()
Nestorowa et al. (2016) Mouse haematopoietic stem cells 1920 NestorowaHSCData()
Romanov et al. (2017) Mouse brain 2881 RomanovBrainData()
Segerstolpe et al. (2016) Human pancreas 3514 SegerstolpePancreasData()
Shekhar et al. (2016) Mouse retina 44994 ShekharRetinaData()
Usoskin et al. (2015) Mouse brain 864 UsoskinBrainData()
Tasic et al. (2016) Mouse brain 1809 TasicBrainData()
Xin et al. (2016) Human pancreas 1600 XinPancreasData()
Zeisel et al. (2015) Mouse brain 3005 ZeiselBrainData()

3 Adding new data sets

Please contact us if you have a data set that you would like to see added to this package. The only requirement is that your data set has publicly available expression values (ideally counts) and sample annotation. The more difficult/custom the format, the better, as its inclusion in this package will provide more value for other users in the R/Bioconductor community.

If you have already written code that processes your desired data set in a SingleCellExperiment-like form, we would welcome a pull request here. The process can be expedited by ensuring that you have the following files:

Potential contributors are recommended to examine some of the existing scripts in the package to pick up the coding conventions. Remember, we’re more likely to accept a contribution if it’s indistinguishable from something we might have written ourselves!

As a general rule, 10X Genomics data sets are not suitable for inclusion in this package. They are either easy to load (e.g., with functions from the DropletUtils package), or they are more appropriately obtained with dedicated 10X packages like TENxPBMCData or TENxBrainData. That said, inclusion will be considered if the format has been sufficiently customized by the original authors.

References

Baron, M., A. Veres, S. L. Wolock, A. L. Faust, R. Gaujoux, A. Vetere, J. H. Ryu, et al. 2016. “A Single-Cell Transcriptomic Map of the Human and Mouse Pancreas Reveals Inter- and Intra-cell Population Structure.” Cell Syst 3 (4):346–60.

Campbell, J. N., E. Z. Macosko, H. Fenselau, T. H. Pers, A. Lyubetskaya, D. Tenen, M. Goldman, et al. 2017. “A molecular census of arcuate hypothalamus and median eminence cell types.” Nat. Neurosci. 20 (3):484–96.

Chen, R., X. Wu, L. Jiang, and Y. Zhang. 2017. “Single-Cell RNA-Seq Reveals Hypothalamic Cell Diversity.” Cell Rep 18 (13):3227–41.

Grun, D., M. J. Muraro, J. C. Boisset, K. Wiebrands, A. Lyubimova, G. Dharmadhikari, M. van den Born, et al. 2016. “De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data.” Cell Stem Cell 19 (2):266–77.

La Manno, G., D. Gyllborg, S. Codeluppi, K. Nishimura, C. Salto, A. Zeisel, L. E. Borm, et al. 2016. “Molecular Diversity of Midbrain Development in Mouse, Human, and Stem Cells.” Cell 167 (2):566–80.

Lawlor, N., J. George, M. Bolisetty, R. Kursawe, L. Sun, V. Sivakamasundari, I. Kycia, P. Robson, and M. L. Stitzel. 2017. “Single-cell transcriptomes identify human islet cell signatures and reveal cell-type-specific expression changes in type 2 diabetes.” Genome Res. 27 (2):208–22.

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.

Mahata, B., X. Zhang, A. A. Kolodziejczyk, V. Proserpio, L. Haim-Vilmovsky, A. E. Taylor, D. Hebenstreit, et al. 2014. “Single-cell RNA sequencing reveals T helper cells synthesizing steroids de novo to contribute to immune homeostasis.” Cell Rep 7 (4):1130–42.

Marques, S., A. Zeisel, S. Codeluppi, D. van Bruggen, A. Mendanha Falcao, L. Xiao, H. Li, et al. 2016. “Oligodendrocyte heterogeneity in the mouse juvenile and adult central nervous system.” Science 352 (6291):1326–9.

Muraro, M. J., G. Dharmadhikari, D. Grun, N. Groen, T. Dielen, E. Jansen, L. van Gurp, et al. 2016. “A Single-Cell Transcriptome Atlas of the Human Pancreas.” Cell Syst 3 (4):385–94.

Nestorowa, S., F. K. Hamey, B. Pijuan Sala, E. Diamanti, M. Shepherd, E. Laurenti, N. K. Wilson, D. G. Kent, and B. Gottgens. 2016. “A single-cell resolution map of mouse hematopoietic stem and progenitor cell differentiation.” Blood 128 (8):20–31.

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.

Romanov, R. A., A. Zeisel, J. Bakker, F. Girach, A. Hellysaz, R. Tomer, A. Alpar, et al. 2017. “Molecular interrogation of hypothalamic organization reveals distinct dopamine neuronal subtypes.” Nat. Neurosci. 20 (2):176–88.

Segerstolpe, A., A. Palasantza, P. Eliasson, E. M. Andersson, A. C. Andreasson, X. Sun, S. Picelli, et al. 2016. “Single-Cell Transcriptome Profiling of Human Pancreatic Islets in Health and Type 2 Diabetes.” Cell Metab. 24 (4):593–607.

Shekhar, K., S. W. Lapan, I. E. Whitney, N. M. Tran, E. Z. Macosko, M. Kowalczyk, X. Adiconis, et al. 2016. “Comprehensive Classification of Retinal Bipolar Neurons by Single-Cell Transcriptomics.” Cell 166 (5):1308–23.

Tasic, B., V. Menon, T. N. Nguyen, T. K. Kim, T. Jarsky, Z. Yao, B. Levi, et al. 2016. “Adult mouse cortical cell taxonomy revealed by single cell transcriptomics.” Nat. Neurosci. 19 (2):335–46.

Usoskin, D., A. Furlan, S. Islam, H. Abdo, P. Lonnerberg, D. Lou, J. Hjerling-Leffler, et al. 2015. “Unbiased classification of sensory neuron types by large-scale single-cell RNA sequencing.” Nat. Neurosci. 18 (1):145–53.

Xin, Y., J. Kim, H. Okamoto, M. Ni, Y. Wei, C. Adler, A. J. Murphy, G. D. Yancopoulos, C. Lin, and J. Gromada. 2016. “RNA Sequencing of Single Human Islet Cells Reveals Type 2 Diabetes Genes.” Cell Metab. 24 (4):608–15.

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.