Package 'clustermole'

Title: Unbiased Single-Cell Transcriptomic Data Cell Type Identification
Description: Assignment of cell type labels to single-cell RNA sequencing (scRNA-seq) clusters is often a time-consuming process that involves manual inspection of the cluster marker genes complemented with a detailed literature search. This is especially challenging when unexpected or poorly described populations are present. The clustermole R package provides methods to query thousands of human and mouse cell identity markers sourced from a variety of databases.
Authors: Igor Dolgalev [aut, cre]
Maintainer: Igor Dolgalev <[email protected]>
License: MIT + file LICENSE
Version: 1.1.1.9000
Built: 2024-11-03 05:09:18 UTC
Source: https://github.com/igordot/clustermole

Help Index

Cell types based on the expression of all genes

Description

Perform enrichment of cell type signatures based on the full gene expression matrix.

Usage

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

Arguments

expr_mat

Expression matrix (logCPMs, logFPKMs, or logTPMs) with genes as rows and clusters/populations/samples as columns.
species

Species: hs for human or mm for mouse.
method

Enrichment method: ssgsea, gsva, singscore, or all. The method to use for the estimation of gene set enrichment scores. The options are ssGSEA (Barbie et al, 2009), GSVA (Hänzelmann et al, 2013), singscore (Foroutan et al, 2018), or a combination of all three methods.

Value

A data frame of enrichment results.

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 the full list of cell type markers in the clustermole database.

Usage

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

Arguments

species

Species: hs for human or mm for mouse.

Value

A data frame of cell type markers (one gene per row).

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)

Arguments

genes

A vector of genes.
species

Species: hs for human or mm for mouse.

Value

A data frame of enrichment results with hypergeometric test p-values.

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

file

A connection object or a character string (can be a URL).
geneset_label

Column name for gene sets (first column of the GMT file) in the output data frame.
gene_label

Column name for genes (variable columns of the GMT file) in the output data frame.

Value

A data frame with gene sets as the first column and genes as the second column (one gene per row).

Examples

gmt <- "http://software.broadinstitute.org/gsea/msigdb/supplemental/scsig.all.v1.0.symbols.gmt"
gmt_tbl <- read_gmt(gmt)
head(gmt_tbl)