View source: R/JessaBrainData.R
JessaBrainData | R Documentation |
Obtain the mouse brain single-cell RNA-seq dataset from Jessa et al. (2019).
JessaBrainData(filtered = TRUE, location = TRUE, legacy = FALSE)
filtered |
Logical scalar indicating whether to filter out cells that were not used by the authors. |
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. |
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.
The column data contains sample of origin, some QC metrics and various cluster assignments for each cell.
Cluster assignments starting with Sample_*
are derived from per-sample analyses and cannot be compared sensibly across samples.
Other clusterings (Forebrain_*
and Pons_*
) are derived from joint analyses across all samples involving the named tissue.
The reducedDims
of the output contains various dimensionality reduction results.
Coordinates for entries prefixed with Sample_*
were generated from per-sample analyses and cannot be compared across samples.
Coordinates for entries prefixed with Forebrain_*
and Pons_*
were generated from joint analyses from the corresponding tissue.
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/jessa-brain
.
A SingleCellExperiment object with a single matrix of UMI counts.
Aaron Lun
Jessa S et al. (2019). Stalled developmental programs at the root of pediatric brain tumors Nat Genet 51, 1702-1713
if (.Machine$sizeof.pointer > 4) { # too large for 32-bit machines!
sce <- JessaBrainData()
}
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