Description

Measuring Cell Type Similarity with Gene Ontology in Single-Cell RNA-Seq.

Description

Traditional methods for analyzing single cell RNA-seq datasets focus solely on gene expression, but this package introduces a novel approach that goes beyond this limitation. Using Gene Ontology terms as features, the package allows for the functional profile of cell populations, and comparison within and between datasets from the same or different species. Our approach enables the discovery of previously unrecognized functional similarities and differences between cell types and has demonstrated success in identifying cell types' functional correspondence even between evolutionarily distant species.

README.md

cran.r-project.org

scGOclust

Leveraging Gene Ontology to Measure Cell Type Similarity Between Single Cell RNA-Seq Datasets

Author & Maintainer: Yuyao Song [email protected]

cran downloads

Note main branch version is compatible with Seurat and SeuratObject >= V5.0. If you are still using V4.0, please go to release scGOclust_V0.1.3.

Installation

First create the conda environment. Mamba is recommended as a faster alternative for conda.
conda env create -f scGOclust_conda_7Dec2022.yml
Then, open R under this environment, and install several packages not in conda:
remotes::install_github('satijalab/seurat-wrappers'), install.packages("pheatmap", "slanter")
Finally, install scGOclust from GitHub:
devtools::install_github("Papatheodorou-Group/scGOclust", ref = "main")

Usage

This package operates on pairs of Seurat objects

Refer to vignettes for usage examples

r-scGOclust

scGOclust

Leveraging Gene Ontology to Measure Cell Type Similarity Between Single Cell RNA-Seq Datasets

Installation

Usage

License: GPLv3.0

Version

License

Status

Source

Homepage

Platforms (75)

scGOclust

Leveraging Gene Ontology to Measure Cell Type Similarity Between Single Cell RNA-Seq Datasets

Installation

Usage

License: GPLv3.0

Version

License

Status

Source

Homepage

Platforms75 (75)

Platforms (75)