CellID Bioconductor

Introduction

CellID is an R package that provides a collection of methods to identify cell types in single-cell RNA-sequencing data using reference transcriptome libraries. It uses a machine learning approach to classify cells based on their gene expression profiles, allowing for high-throughput and high-accuracy cell type identification.

Installation

CellID can be installed from the Bioconductor repository using the following code:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("CellID")

Usage

After installation, CellID can be loaded into your R environment with the following code:

library(CellID)

The main function in CellID is cellid, which takes two arguments: the expression matrix of your single-cell RNA-seq data and a reference transcriptome library. The library can either be a pre-built library provided by CellID or a custom library built from your own data.

# Load example single-cell data
data(sce_celseq)

# Load reference transcriptome library
library(Celegans)

# Run CellID classification
classification <- cellid(sce_celseq, dataset = Celegans, verbose = FALSE)

The classification output is a list containing the predicted cell type for each cell in your input data.

Conclusion

CellID makes cell type identification in single-cell RNA-seq data simple and efficient through its use of machine learning classification. Its integration with Bioconductor ensures that CellID is a reliable and well-maintained package for cell type discovery.