analyse_sc_clusters,Seurat-method {ReactomeGSA} | R Documentation |
Analyses cell clusters of a single-cell RNA-sequencing experiment to get pathway-level expressions for every cluster of cells.
## S4 method for signature 'Seurat' analyse_sc_clusters( object, use_interactors = TRUE, include_disease_pathways = FALSE, create_reactome_visualization = FALSE, create_reports = FALSE, report_email = NULL, verbose = FALSE, assay = "RNA", slot = "counts", ... )
object |
The |
use_interactors |
If set (default), protein-protein interactors from IntAct are used to extend Reactome pathways. |
include_disease_pathways |
If set, disease pathways are included as well. Disease pathways in Reactome follow a different annotation approach and can therefore lead to inaccurate results. |
create_reactome_visualization |
If set, the interactive visualization in Reactome's PathwayBrowser is created. |
create_reports |
If set, PDF and Microsoft Excel reports are created. Links to these report files are send to the supplied e-mail address. |
report_email |
The e-mail address to which reports should be sent to. |
verbose |
If set, additional status messages are printed. |
assay |
By default, the "RNA" assay is used, which contains the original read counts. |
slot |
The slot in the Seurat object to use. Default and recommended approach is to use the raw counts. |
... |
Parameters passed to the specific implementation. Detailed documentations can be found there. |
There are currently two specific implementations of
this function, one to support Seurat
objects
and one to support Bioconductor's SingleCellExperiment
class.
A ReactomeAnalysisResult
object.
# This example shows how a Seurat object can be analysed # the approach is identical for SingleCellExperiment objects library(ReactomeGSA.data) data(jerby_b_cells) # perform the GSVA analysis gsva_result <- analyse_sc_clusters(jerby_b_cells, verbose = FALSE)