Introduction to clustermole

Overview

The clustermole R package is designed to simplify the assignment of cell type labels to unknown cell populations, such as scRNA-seq clusters. It provides methods to query cell identity markers sourced from a variety of databases. The package includes three primary features:

• a meta-database of human and mouse markers for thousands of cell types (clustermole_markers())
• cell type prediction based on a set of marker genes (clustermole_overlaps())
• cell type prediction based on a table of expression values (clustermole_enrichment())

Setup

You can install clustermole from CRAN.

 install.packages("clustermole")

Load clustermole.

 library(clustermole)

Cell type markers

You can use clustermole as a simple database and get a data frame of all cell type markers.

markers <- clustermole_markers(species = "hs")
markers
 #> # A tibble: 422,292 ×ばつ 8
 #> celltype_full db species organ celltype n_genes gene_origi...1 gene 
 #> <chr> <chr> <chr> <chr> <chr> <int> <chr> <chr>
 #> 1 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 ACCSL ACCSL
 #> 2 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 ACVR1B ACVR...
 #> 3 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 ASF1B ASF1B
 #> 4 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 BCL2L10 BCL2...
 #> 5 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 BLCAP BLCAP
 #> 6 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 CASC3 CASC3
 #> 7 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 CLEC10A CLEC...
 #> 8 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 CNOT11 CNOT...
 #> 9 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 DCLK2 DCLK2
 #> 10 1-cell stage cell (B... Cell... Human Embr... 1-cell ... 32 DHCR7 DHCR7
 #> # i 422,282 more rows
 #> # i abbreviated name: 1​gene_original

Each row contains a gene and a cell type associated with it. The gene column is the gene symbol and the celltype_full column contains the full cell type string, including the species and the original database. Human or mouse versions can be retrieved.

Many tools that works with gene sets require input as a list. To convert the markers from a data frame to a list, you can use gene as the values and celltype_full as the grouping variable.

markers_list <- split(x = markers$gene, f = markers$celltype_full)

Cell types based on marker genes

If you have a character vector of genes, such as cluster markers, you can compare them to known cell type markers to see if they overlap any of the known cell type markers (overrepresentation analysis).

my_overlaps <- clustermole_overlaps(genes = my_genes_vec, species = "hs")

Cell types based on an expression matrix

If you have expression values, such as average expression for each cluster, you can perform cell type enrichment based on the full gene expression matrix (log-transformed CPM/TPM/FPKM values). The matrix should have genes as rows and clusters/samples as columns. The underlying enrichment method can be changed using the method parameter.

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