analyse_sc_clusters-Seurat-method: analyse_sc_clusters - Seurat

In reactome/ReactomeGSA: Client for the Reactome Analysis Service for comparative multi-omics gene set analysis

analyse_sc_clusters - Seurat

Description

Analyses cell clusters of a single-cell RNA-sequencing experiment to get pathway-level expressions for every cluster of cells.

Usage

## 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",
  ...
)

Arguments

object	The Seurat object containing the single cell RNA-sequencing data.
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.

Details

There are currently two specific implementations of this function, one to support Seurat objects and one to support Bioconductor's SingleCellExperiment class.

Value

A ReactomeAnalysisResult object.

Examples

# 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)

reactome/ReactomeGSA documentation built on Nov. 13, 2023, 11:13 p.m.