---
title: "Introduction to clustermole"
output:
  rmarkdown::html_vignette:
    keep_md: true
vignette: >
  %\VignetteIndexEntry{Introduction to clustermole}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include=FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
# reduce the minimum number of characters for the tibble column titles (default: 15)
options(pillar.min_title_chars = 10)
```

## 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](https://cran.r-project.org/package=clustermole).

```{r install-package, eval=FALSE}
install.packages("clustermole")
```

Load clustermole.

```{r load-package, message=FALSE, warning=FALSE}
library(clustermole)
```

## Cell type markers

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

```{r markers}
markers <- clustermole_markers(species = "hs")
markers
```

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.

```{r celltypes-list}
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).

```{r overlaps, eval=FALSE}
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.

```{r enrichment, eval=FALSE}
my_enrichment <- clustermole_enrichment(expr_mat = my_expr_mat, species = "hs")
```