BrainCellR: A Precise Cell Type Nomenclature R Package for Comparative Analysis Across Brain Single-Cell Datasets

BrainCellR provides researchers with a powerful and user-friendly package for efficient cell type classfication and nomination of single-cell transcriptomic data in the brain.

BrainCellR process comprises the following steps:

• Preparation of input data
• Fine-scale clustering of single-cell data
• Provision of clusters to a supervised cell type classifier capable of outputting major cell classes and subclasses
• Identification of differentially expressed genes and select marker genes from the identified differentially expressed genes
  • Find differentially expressed genes
  • Selection of genes that exhibit a high fold change value and are expressed in the majority of cells within a given cell type
  • Ranking of genes based on specific score
• Acquisition of the final cell type annotation
• Identification of markers that cannot be detected using the ROC method

Installation

BrainCellR has a dependencie from Github:

devtools::install_github("AllenInstitute/scrattch.hicat")

Also, Seurat, WGCNA, SingleR, doParallel, foreach, matrixStats, impute, preprocessCore.

BrainCellR can be installed with:

devtools::install_github("WangLab-SINH/BrainCellR")

Tutorials

Tutorials can be seen at https://wanglab-sinh.github.io/braincellr
Data used in tutorial can be downloaded from https://github.com/WangLab-SINH/WangLab-SINH.github.io

Reference data for cell type class and subclass classification can be obtained from https://drive.google.com/drive/folders/1q9JT0JFhBvc6CvkbzZVXmEANXfgQ8VID?usp=sharing

You can contact chiyuhao2018@sinh.ac.cn for any questions.