CITEseq

function assembles data on-the-fly from ExperimentHub to provide a MultiAssayExperiment container. Actually the dataType argument provides access to the available datasets associated to the package.

Usage

CITEseq(
  DataType = c("cord_blood", "peripheral_blood"),
  modes = "*",
  version = "1.0.0",
  dry.run = TRUE,
  filtered = FALSE,
  verbose = TRUE,
  DataClass = c("MultiAssayExperiment", "SingleCellExperiment"),
  ...
)

Arguments

DataType

character(1) indicating the identifier of the dataset to retrieve. (default "cord_blood")

modes

character() The assay types or modes of data to obtain these include scADT and scRNA-seq data by default.

version

character(1) Either version '1.0.0' depending on data version required.

dry.run

logical(1) Whether to return the dataset names before actual download (default TRUE)

filtered

logical(1) indicating if the returned dataset needs to have filtered cells. See Details for additional information about the filtering process.

verbose

logical(1) Whether to show the dataset currently being (down)loaded (default TRUE)

DataClass

either MultiAssayExperiment or SingleCellExperiment data classes can be returned (default MultiAssayExperiment)

...

Additional arguments passed on to the ExperimentHub-class constructor

Value

A single cell multi-modal MultiAssayExperiment or informative data.frame when dry.run is TRUE. When DataClass is SingleCellExperiment an object of this class is returned with an RNA assay as main experiment and other assay(s) as AltExp(s).

Details

CITEseq data are a combination of single cell transcriptomics and about a hundread of cell surface proteins. Available datasets are:

• cord_blood: a dataset of single cells of cord blood as provided in Stoeckius et al. (2017).

  • scRNA_Counts - Stoeckius scRNA-seq gene count matrix

  • scADT - Stoeckius antibody-derived tags (ADT) data

• peripheral_blood: a dataset of single cells of peripheral blood as provided in Mimitou et al. (2019). We provide two different conditions controls (CTRL) and Cutaneous T-cell Limphoma (CTCL). Just build appropriate modes regex for subselecting the dataset modes.

  • scRNA - Mimitou scRNA-seq gene count matrix

  • scADT - Mimitou antibody-derived tags (ADT) data

  • scHTO - Mimitou Hashtag Oligo (HTO) data

  • TCRab - Mimitou T-cell Receptors (TCR) alpha and beta available through the object metadata.

  • TCRgd - Mimitou T-cell Receptors (TCR) gamma and delta available through the object metadata.

If filtered parameter is FALSE (default), the colData of the returned object contains multiple columns of logicals indicating the cells to be discarded. In case filtered is TRUE, the discard column is used to filer the cells. Column adt.discard indicates the cells to be discarded computed on the ADT assay. Column mito.discard indicates the cells to be discarded computed on the RNA assay and mitocondrial genes. Column discard combines the previous columns with an OR operator. Note that for the peripheral_blood dataset these three columns are computed and returned separately for the CTCL and CTRL conditions. In this case the additional discard column combines the discard.CTCL and discard.CTRL columns with an OR operator. Cell filtering has been computed for cord_blood and peripheral_blood datasets following section 12.3 of the Advanced Single-Cell Analysis with Bioconductor book. Executed code can be retrieved in the CITEseq_filtering.R script of this package.

References

Stoeckius et al. (2017), Mimitou et al. (2019)

Author

Dario Righelli

Examples

mae <- CITEseq(DataType="cord_blood", dry.run=FALSE)
#> Dataset: cord_blood
#> Working on: scADT_Counts
#> Working on: scRNAseq_Counts
#> Working on: coldata_scRNAseq
#> Working on: scADT_clrCounts
#> see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation
#> loading from cache
#> see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation
#> loading from cache
#> see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation
#> loading from cache
#> see ?SingleCellMultiModal and browseVignettes('SingleCellMultiModal') for documentation
#> loading from cache
#> Warning: 'ExperimentList' contains 'data.frame' or 'DataFrame',
#>   potential for errors with mixed data types
#> harmonizing input:
#>   removing 2277 sampleMap rows with 'primary' not in colData
experiments(mae)
#> ExperimentList class object of length 3:
#>  [1] scADT: matrix with 13 rows and 7858 columns
#>  [2] scADT_clr: matrix with 13 rows and 7858 columns
#>  [3] scRNAseq: matrix with 36280 rows and 7858 columns