# 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
## 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):

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:

• ReprocessedFluidigmData() provides 65 cells from Pollen et al. (2014).
• ReprocessedTh2Data() provides 96 T helper cells from Mahata et al. (2014).
• ReprocessedAllenData() provides 379 cells from the mouse visual cortex, which is a subset of the data from Tasic et al. (2016).

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.

• TasicBrainData() provides 1809 cells (and some control libraries) from the mouse brain (Tasic et al. 2016).
• ZeiselBrainData() provides 3005 cells from the mouse brain (Zeisel et al. 2015).
• SegerstolpePancreasData() provides 3514 cells from human pancreas (Segerstolpe et al. 2016).

# 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:

• inst/scripts/make-X-Y-data.Rmd, a Rmarkdown report that creates all components of a SingleCellExperiment. X should be the last name of the first author of the relevant study while Y should be the name of the biological system.
• inst/scripts/make-X-Y-metadata.R, an R script that creates a metadata CSV file at inst/extdata/metadata-X-Y.csv. Metadata files should follow the format described in the ExperimentHub documentation.
• R/XYData.R, an R source file that defines a function XYData() to download the components from ExperimentHub and creates a SingleCellExperiment object.

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

Mahata, B, X Zhang, AA Kolodziejczyk, V Proserpio, L Haim-Vilmovsky, AE Taylor, D Hebenstreit, et al. 2014. “Single-Cell RNA Sequencing Reveals T Helper Cells Synthesizing Steroids de Novo to Contribute to Immune Homeostasis.” Cell Reports 7 (4):1130–42.

Pollen, AA, TJ Nowakowski, J Shuga, X Wang, AA Leyrat, JH Lui, N Li, et al. 2014. “Low-Coverage Single-Cell mRNA Sequencing Reveals Cellular Heterogeneity and Activated Signaling Pathways in Developing Cerebral Cortex.” Nature Biotechnology 32 (10):1053–8.

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.

Tasic, B, V Menon, TN Nguyen, TK Kim, T Jarsky, Z Yao, B Levi, et al. 2016. “Adult Mouse Cortical Cell Taxonomy Revealed by Single Cell Transcriptomics.” Nature Neuroscience 19:335–46.

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