Package {gRaphiaExtra}

Type: Package
Title: An R Package for Integrating 'Seurat' Objects into 'gRaphia'
Version: 0.26.9
Maintainer: Nilabhra R Das <n.das@uq.edu.au>
Description: Utilising graph-based network analysis frameworks, 'Graphia' https://graphia.app/ is a powerful open source visual analytics application developed to aid the interpretation of large and complex datasets. For more details, see article by Freeman et al. (2022) <doi:10.1371/journal.pcbi.1010310>. 'gRaphia' is an extension of the 'Graphia' application within the R environment, providing tools for network analysis and visualisation. 'gRaphiaExtra' provides additional functionality specifically designed for single-cell RNA-sequencing data, enabling users to seamlessly integrate and utilise existing 'Seurat' analysis outputs in 'gRaphia'. The package also provides supplementary functions to support and enhance the 'gRaphia' analysis framework.
License: GPL-3
Depends: R (≥ 4.4.2.0)
Imports: checkmate, dplyr, stats, utils
Encoding: UTF-8
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-09-10 19:29:44 UTC; nilabhra.das
Author: Nilabhra R Das ORCID iD [cre, aut], Kaitlyn A Flynn ORCID iD [aut], John P Kemp ORCID iD [aut]
Repository: CRAN
Date/Publication: 2026-09-18 11:50:13 UTC

testSeuratClusterEnrichment - Test Seurat Clusters for Enrichment

Description

Performs appropriate tests of enrichment of Seurat clusters for a specified set of genes.

Usage

testSeuratClusterEnrichment(
  markers_list,
  test_set,
  background_set,
  test = "fisher",
  alternative = "greater",
  p_adjust = "bonferroni"
)

Arguments

markers_list

Data frame. A data frame containing two columns: a gene column and a cluster column. Typically, output from the FindAllMarkers() function. If using a custom markers_list object, ensure no clusters are labelled as "0". If so, rename that cluster to prevent erroneous enrichment. "0" is a cluster label internally reserved for the background set.

test_set

Data frame or character. Either a data frame including two columns, "gene" and "group" (e.g., causal genes for different mouse diseases), or a vector of genes giving the test set (e.g., mouse breast cancer genes).

background_set

Character. A vector of genes that form the background population for the tests (e.g., all mouse protein coding genes).

test

Character. One of "fisher" (default) for fisher's exact test, "chi.sq" for the chi squared test, "hyper" for the hypergeometric test, and "conditional" for partial odds ratios from a multivariable logistic regression. Conditional is only applicable when groups are included in the test set and there is substantial overlap between the groups.

alternative

Character. One of "greater" (default), "two.sided", or "less".

p_adjust

Character. Correct for multiple testing using one of "holm", "hochberg", "hommel", "bonferroni" (default), "BH", "BY", or "fdr" methods.

Value

A data frame with enrichment test results.

Examples

# Define Background Set
background_set <- c(paste0("gene", 1:20), "gene_causal1", "gene_causal2", "gene_causal3")

# Define Markers List
# Note: Cluster labels must avoid "0" as specified in your function documentation
markers_list <- data.frame(gene = c("gene1", "gene2", "gene_causal1", "gene_causal2",
                                    "gene3", "gene4", "gene5", "gene_causal1",
                                    "gene6", "gene7", "gene8"),
                           cluster = c(rep("Cell Type 1", 4),
                                       rep("Cell Type 2", 4),
                                       rep("Cell Type 3", 3)),
                                       stringsAsFactors = FALSE)

# Define Test Set
# Data frame with 'gene' and 'group' columns
test_set_df <- data.frame(gene = c("gene_causal1", "gene_causal2", "gene_causal3"),
                          group = c("Disease_A", "Disease_A", "Disease_B"),
                          stringsAsFactors = FALSE)

# Example Function Call
# One-sided (greater) Fisher's exact test with Bonferroni correction
testSeuratClusterEnrichment(markers_list = markers_list,
                            test_set = test_set_df,
                            background_set = background_set)