The goal of Inkwell is to provide a collection of curated cell-type specific genesets for use in (manual) classification of single-cell RNA-seq data. For methods on assigning these signatures to cells, see Hancock.
You can install Inkwell from Github with:
devtools::install_github("robertamezquita/Inkwell")
Inkwell stores curated genesets derived from online resources and publications for use with outside methods of annotation. This package makes use of the Bioconductor GSEABase
to store signatures as GeneSetCollection
and GeneSet
class objects. For more details, check out the associated vignette and reference manual.
Briefly, stored genesets are provided as data provided upon loading Inkwell.
library(Inkwell)
MCPcounter_markers
#> GeneSetCollection
#> names: T cells, CD8 T cells, ..., Fibroblasts (10 total)
#> unique identifiers: CD28, CD3D, ..., TAGLN (111 total)
#> types in collection:
#> geneIdType: SymbolIdentifier (1 total)
#> collectionType: NullCollection (1 total)
To create your own genesets, see data-raw/template.R
for a quick template on essential fields and descriptions for creating a manually curated GeneSetCollection
, and the data-raw/pbmc3k_markers.R
script for how the example pbmc3k_markers
data object was created.
Future iterations will utilize additional capabilities of the GSEABase
package provided classes, such as GeneColorSet
to provide additional annotation of genes such as expression status or confidence estimates. Furthermore, in addition to manually curated sets, select calculated sets may be included in future iterations, which could include such additional rich information about (differential) expression status.
Methods to create and cache genesets may also be implemented in future iterations to expand the scope of the package beyond manually curated genesets.
Below are the data available in the package, where the data column refers to the object that is accessible upon loading Inkwell.
| data | description | source |
|------------------|------------------------------------------------------------------------|---------------------------------------------------|
| pbmc3k_markers
| Markers from Satija lab tutorial for Seurat using PBMC 3k 10X dataset. | https://satijalab.org/seurat/pbmc3k_tutorial.html |
| MCPcounter_markers
| Markers from Becht et al. 2016 defining major canonical cell-types | https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1070-5 |
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