scRNAseq 2.8.0
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):
## mainExpName: NULL
## altExpNames(0):Readers are referred to the SummarizedExperiment and SingleCellExperiment documentation
for further information on how to work with SingleCellExperiment objects.
The listDatasets() function returns all available datasets in scRNAseq,
along with some summary statistics and the necessary R command to load them.
out <- listDatasets()| Reference | Taxonomy | Part | Number | Call |
|---|---|---|---|---|
| Aztekin et al. (2019) | Xenopus | tail | 13199 | AztekinTailData() |
| Bach et al. (2017) | Mouse | mammary gland | 25806 | BachMammaryData() |
| Bacher et al. (2020) | Human | T cells | 104417 | BacherTCellData() |
| Baron et al. (2016) | Human | pancreas | 8569 | BaronPancreasData('human') |
| Baron et al. (2016) | Mouse | pancreas | 1886 | BaronPancreasData('mouse') |
| Bhaduri et al. (2020) | Human | cortical organoids | 242349 | BhaduriOrganoidData() |
| Buettner et al. (2015) | Mouse | embryonic stem cells | 288 | BuettnerESCData() |
| (???) | Human | haematopoietic stem and progenitor | 5183 | BunisHSPCData() |
| Campbell et al. (2017) | Mouse | brain | 21086 | CampbellBrainData() |
| Chen et al. (2017) | Mouse | brain | 14437 | ChenBrainData() |
| Darmanis et al. (2015) | Human | brain | 466 | DarmanisBrainData() |
| Ernst et al. (2019) | Mouse | testis | 68937 | ErnstSpermatogenesisData() |
| Fletcher et al. (2017) | Mouse | olfactory epithelium | 616 | FletcherOlfactoryData() |
| Grun et al. (2016) | Mouse | haematopoietic stem cells | 1915 | GrunHSCData() |
| Grun et al. (2016) | Human | pancreas | 1728 | GrunPancreasData() |
| Giladi et al. (2018) | Mouse | haematopoietic stem cells | 81024 | GiladiHSCData(mode='rna') |
| He et al. (2020) | Human | various organs | 84363 | HeOrganAtlasData() |
| Hermann et al. (2018) | Mouse | spermatogenic cells | 2325 | HermannSpermatogenesisData() |
| Hu et al. (2017) | Mouse | cortex | 48000 | HuCortexData() |
| Kolodziejczyk et al. (2015) | Mouse | embryonic stem cells | 704 | KolodziejczykESCData() |
| Jessa et al. (2019) | Mouse | brain | 61595 | JessaBrainData() |
| 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() |
| Ledergor et al. (2018) | Human | bone marrow plasma cells | 51840 | LedergorMyelomaData() |
| Leng et al. (2015) | Human | embryonic stem cells | 460 | LengESCData() |
| Lun et al. (2017) | Mouse | 416B cells | 192 | LunSpikeInData('416b') |
| Lun et al. (2017) | Mouse | trophoblasts | 192 | LunSpikeInData('tropho') |
| Macosko et al. (2015) | Mouse | retina | 49300 | MacoskoRetinaData() |
| Mahata et al. (2014) | Mouse | T helper cells | 96 | ReprocessedTh2Data() |
| Mair et al. (2020) | Human | peripheral blood mononuclear cells | 29033 | MairPBMCData() |
| Kotliarov et al. (2020) | Human | peripheral blood mononuclear cells | 58654 | KotliarovPBMCData() |
| Marques et al. (2016) | Mouse | brain | 5069 | MarquesBrainData() |
| Messmer et al. (2019) | Human | embryonic stem cells | 1344 | MessmerESCData() |
| Muraro et al. (2016) | Human | pancreas | 3072 | MuraroPancreasData() |
| Nestorowa et al. (2016) | Mouse | haematopoietic stem cells | 1920 | NestorowaHSCData() |
| Nowakowski et al. (2017) | Human | cortex | 4261 | NowakowskiCortexData() |
| Paul et al. (2015) | Mouse | haematopoietic stem cells | 10368 | PaulHSCData() |
| Pollen et al. (2014) | Human | cortex | 65 | ReprocessedFluidigmData() |
| Pollen et al. (2015) | Human | outer radial glia | 367 | PollenGliaData() |
| Richard et al. (2018) | Mouse | CD8+ T cells | 572 | RichardTCellData() |
| Romanov et al. (2017) | Mouse | brain | 2881 | RomanovBrainData() |
| Segerstolpe et al. (2016) | Human | pancreas | 3514 | SegerstolpePancreasData() |
| Shekhar et al. (2016) | Mouse | retina | 44994 | ShekharRetinaData() |
| Stoeckius et al. (2018) | Mouse | peripheral blood mononuclear cells | 50000 | StoeckiusHashingData(mode='mouse') |
| Stoeckius et al. (2018) | Human | peripheral blood mononuclear cells | 50000 | StoeckiusHashingData(mode='human') |
| Stoeckius et al. (2018) | Human | HEK, THP1, K562, KG1 cells | 30000 | StoeckiusHashingData(type='mixed') |
| Usoskin et al. (2015) | Mouse | brain | 864 | UsoskinBrainData() |
| Tasic et al. (2016) | Mouse | brain | 1809 | TasicBrainData() |
| Tasic et al. (2016) | Mouse | visual cortex | 379 | ReprocessedAllenData() |
| Wu et al. (2019) | Mouse | kidney | 17542 | WuKidneyData() |
| Xin et al. (2016) | Human | pancreas | 1600 | XinPancreasData() |
| Zeisel et al. (2015) | Mouse | brain | 3005 | ZeiselBrainData() |
| Zeisel et al. (2018) | Mouse | nervous system | 160796 | ZeiselNervousData() |
| Zhao et al. (2020) | Human | liver immune cells | 68100 | ZhaoImmuneLiverData() |
| Zhong et al. (2018) | Human | prefrontal cortex | 2394 | ZhongPrefrontalData() |
| Zilionis et al. (2019) | Human | lung | 173954 | ZilionisLungData() |
| Zilionis et al. (2019) | Mouse | lung | 17549 | ZilionisLungData('mouse') |
If the original dataset was not provided with Ensembl annotation, we can map the identifiers with ensembl=TRUE.
Any genes without a corresponding Ensembl identifier is discarded from the dataset.
sce <- ZeiselBrainData(ensembl=TRUE)
head(rownames(sce))## [1] "ENSMUSG00000029669" "ENSMUSG00000046982" "ENSMUSG00000039735"
## [4] "ENSMUSG00000033453" "ENSMUSG00000046798" "ENSMUSG00000034009"Functions also have a location=TRUE argument that loads in the gene coordinates.
sce <- ZeiselBrainData(ensembl=TRUE, location=TRUE)
head(rowRanges(sce))## GRanges object with 6 ranges and 2 metadata columns:
## seqnames ranges strand | featureType
## <Rle> <IRanges> <Rle> | <character>
## ENSMUSG00000029669 6 21771395-21852515 - | endogenous
## ENSMUSG00000046982 18 84011627-84087706 - | endogenous
## ENSMUSG00000039735 3 122538719-122619715 - | endogenous
## ENSMUSG00000033453 9 30899155-30922452 - | endogenous
## ENSMUSG00000046798 5 5489537-5514958 - | endogenous
## ENSMUSG00000034009 3 79641611-79737880 - | endogenous
## originalName
## <character>
## ENSMUSG00000029669 Tspan12
## ENSMUSG00000046982 Tshz1
## ENSMUSG00000039735 Fnbp1l
## ENSMUSG00000033453 Adamts15
## ENSMUSG00000046798 Cldn12
## ENSMUSG00000034009 Rxfp1
## -------
## seqinfo: 118 sequences from GRCm38 genomePlease 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!
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