BacherTCellData: Obtain the Bacher T cell data

In LTLA/scRNAseq: Collection of Public Single-Cell RNA-Seq Datasets

Obtain the Bacher T cell data

Description

Obtain the human COVID T cell single-cell RNA-seq dataset from Bacher et al. (2020).

Usage

BacherTCellData(
  filtered = TRUE,
  ensembl = FALSE,
  location = TRUE,
  legacy = FALSE
)

Arguments

filtered	Logical scalar indicating whether to filter out cells that were not used by the authors.
ensembl	Logical scalar indicating whether the output row names should contain Ensembl identifiers.
location	Logical scalar indicating whether genomic coordinates should be returned.
legacy	Logical scalar indicating whether to pull data from ExperimentHub. By default, we use data from the gypsum backend.

Details

Column metadata is scraped from GEO, using both the author-supplied TSV of per-cell annotations and the sample-level metadata. This contains information such as the diagnosis, severity, WHO class, clustering and clonotype.

If filtered=TRUE, only the cells used by the authors in their final analysis are returned. Otherwise, an additional filtered field will be present in the colData, indicating whether the cell was retained by the authors.

If ensembl=TRUE, the gene symbols are converted to Ensembl IDs in the row names of the output object. Rows with missing Ensembl IDs are discarded, and only the first occurrence of duplicated IDs is retained.

If location=TRUE, the coordinates of the Ensembl gene models are stored in the rowRanges of the output.

All data are downloaded from ExperimentHub and cached for local re-use. Specific resources can be retrieved by searching for scRNAseq/bacher-tcell.

Value

A SingleCellExperiment object with a single matrix of UMI counts.

Author(s)

Aaron Lun

References

Bacher P et al. (2020). Low avidity T cell responses to SARS-CoV-2 in unexposed individuals and severe COVID-19 Immunity 53, 1258-1271

Examples

if (.Machine$sizeof.pointer > 4) { # too large for 32-bit machines!
    sce <- BacherTCellData()
}

LTLA/scRNAseq documentation built on April 24, 2024, 5:58 p.m.