Description Usage Arguments Details Value Author(s) References See Also Examples
Various single-cell RNA-seq data on PBMC, generated by 10X Genomics.
1 2 3 4 5 | TENxPBMCData(dataset = c("pbmc4k", "pbmc68k",
"frozen_pbmc_donor_a", "frozen_pbmc_donor_b",
"frozen_pbmc_donor_c", "pbmc33k", "pbmc3k",
"pbmc6k", "pbmc8k", "pbmc5k-CITEseq"),
as.sparse = TRUE)
|
dataset |
Which PBMC dataset from 10X Genomics should be retrieved? |
as.sparse |
Logical, specifies whether the underlying HDF5 dataset should
be treated as sparse or not - will be passed to the call to
|
Single-cell RNA-seq and CITE-seq data were generated by 10X Genomics at various times, using different versions of CellRanger, different chemistries and different genome builds. For details, see the 10X website.
We obtained ‘filtered’ data and generated
SingleCellExperiment
containers with data stored as an HDF5
Assay.
As rowData
we include ENSEMBL
and Symbol_TENx
which
are ENSEMBL gene ID and gene symbol provided by TENx genomics and a
remapping of the Ensembl identifier to a Hugo gene symbol as columns
Symbol
using the org.Hs.eg.db
package. The difference
between Symbol
and Symbol_TENx
is that the former has many
missing values (for non-protein coding genes) whereas the later is
technically not a Hugo gene symbol.
In case of CITE-seq data, the rowData
has an additional column Type
specifying if the counts are "Gene Expression" or "Antibody Capture". Note that there is a separate rowData
for the altExp
.
A SingleCellExperiment object with a HDF5Matrix in the counts
assay, which contains UMI counts for each gene in each cell.
Row- and column-level metadata are also provided.
In case of CITE-seq data, the "Antibody Capture" counts are stored in
the altExp
of the SingleCellExperiment.
Kasper D. Hansen, Stephanie Hicks, Davide Risso
10X Genomics (2017). 1.3 Million PBMC Cells from E18 Mice. https://support.10xgenomics.com/single-cell-gene-expression/datasets/1.3.0/1M_neurons
1 2 3 4 5 6 7 8 9 | sce <- TENxPBMCData()
sce
lib.size <- colSums(assay(sce))
hist(log10(lib.size))
# CITE-seq data
sce <- TENxPBMCData(dataset = "pbmc5k-CITEseq")
sce
altExp(sce)
|
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