Package {clustermole}

Type:	Package
Title:	Cell Type Marker Database for Single-Cell RNA-Seq Data
Version:	1.2.0
Description:	Provides a meta-database of thousands of human and mouse cell identity markers curated from multiple sources, along with methods for cell type prediction based on marker gene overlaps or gene set enrichment.
License:	MIT + file LICENSE
URL:	https://igordot.github.io/clustermole/
BugReports:	https://github.com/igordot/clustermole/issues
Depends:	R (≥ 4.3)
Imports:	dplyr (≥ 1.1.0), methods, rlang, stats, tibble, tidyr, utils
Suggests:	covr, GSEABase, GSVA (≥ 1.50.0), knitr, rmarkdown, roxygen2, singscore, testthat
Config/roxygen2/version:	8.1.0
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2026-10-07 02:27:37 UTC; id460
Author:	Igor Dolgalev [aut, cre]
Maintainer:	Igor Dolgalev <igor.dolgalev@nyumc.org>
Repository:	CRAN
Date/Publication:	2026-10-07 02:50:02 UTC

clustermole: Cell Type Marker Database for Single-Cell RNA-Seq Data

Description

Provides a meta-database of thousands of human and mouse cell identity markers curated from multiple sources, along with methods for cell type prediction based on marker gene overlaps or gene set enrichment.

Author(s)

Maintainer: Igor Dolgalev igor.dolgalev@nyumc.org (ORCID)

Authors:

• Igor Dolgalev igor.dolgalev@nyumc.org (ORCID)

See Also

Useful links:

• https://igordot.github.io/clustermole/

• Report bugs at https://github.com/igordot/clustermole/issues

Cell types based on the expression of all genes

Description

Score cell type signatures using the full gene expression matrix.

Usage

clustermole_enrichment(expr_mat, species, method = "gsva", max_rank = 100)

Arguments

Value

A data frame with one row per returned signature and input column:

• cluster: Input column name.

• score: Enrichment score (higher means greater enrichment).

• score_rank: Signature rank (lower means greater enrichment). With method = "all", this is the average rank across methods.

• Signature metadata (see clustermole_markers()).

With method = "all", these columns replace score:

• ⁠score_rank_{method}⁠: The ranks from each method.

• ⁠score_ranks_{stat}⁠: Minimum, mean, and median ranks across methods.

References

Barbie, D., Tamayo, P., Boehm, J. et al. Systematic RNA interference reveals that oncogenic KRAS-driven cancers require TBK1. Nature 462, 108–112 (2009). doi:10.1038/nature08460

Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: Gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14, 7 (2013). doi:10.1186/1471-2105-14-7

Foroutan, M., Bhuva, D.D., Lyu, R. et al. Single sample scoring of molecular phenotypes. BMC Bioinformatics 19, 404 (2018). doi:10.1186/s12859-018-2435-4

Examples

# my_enrichment <- clustermole_enrichment(
#   expr_mat = my_expr_mat, species = "hs"
# )

Available cell type markers

Description

Retrieve cell type markers from the clustermole database.

Usage

clustermole_markers(species = c("hs", "mm"))

Arguments

Details

The gene column uses the official NCBI symbols for the requested species. The package maps the aliases in each source database to these symbols. The gene_original column preserves the symbol from the source. The source databases include both human and mouse cell type markers. The package maps them across species with ortholog data from the Alliance of Genome Resources and keeps only reciprocal best matches. The output does not include genes that have no ortholog in the requested species and does not include signatures with fewer than five genes.

Value

A data frame of cell type markers with these columns:

• gene: Canonical gene symbol for the requested species.

• gene_original: Original source gene symbol.

• celltype_full: Full cell type signature identifier.

• db: Source database.

• celltype: Cell type label.

• organ: Organ label.

• species: Source signature species, if known.

• n_genes: Gene count per signature.

Examples

markers <- clustermole_markers()
head(markers)

Cell types based on overlap of marker genes

Description

Perform overrepresentation analysis for a set of genes compared to all cell type signatures.

Usage

clustermole_overlaps(genes, species, max_p = 0.05, max_fdr = 1)

Arguments

Value

A data frame with one row per returned signature:

• overlap: Unique gene count shared by the input and signature.

• p_value: Hypergeometric test p-value.

• fdr: Benjamini-Hochberg adjusted p-value across all tested signatures.

• n_genes: Unique gene count in the signature for the requested species.

• Signature metadata (see clustermole_markers()).

Examples

my_genes <- c("CD2", "CD3D", "CD3E", "CD3G", "TRAC", "TRBC2", "LTB")
my_overlaps <- clustermole_overlaps(genes = my_genes, species = "hs")
head(my_overlaps)

Read a GMT file into a data frame

Description

Read a GMT file into a data frame

Usage

read_gmt(file, geneset_label = "celltype", gene_label = "gene")

Arguments

Value

A data frame with gene sets and genes, one gene per row.

Examples

## Not run: 
gmt <- "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.all.v1.0.symbols.gmt"
gmt_tbl <- read_gmt(gmt)
head(gmt_tbl)

## End(Not run)