Welcome to the CoVarNet!

CoVarNet is a computational framework aiming to unravel the coordination among multiple cell types by analyzing the covariance in the frequencies of cell types across various samples.

Installation

devtools::install_github(repo = "https://github.com/christophechu/CoVarNet")
library(CoVarNet)

Tutorials

• Discovery of cellular modules in scRNA-seq data
• Recovery of cellular modules in scRNA-seq data and spatial transcriptomics data
• Trajectory inference for individuals

Requirements

The R/Python packages listed below are required for running CoVarNet. These versions are used for testing the CoVarNet code. Other versions might work too.

• R (v4.1.2).
• R packages: dplyr(v1.1.4), NMF(v0.25), Seurat(v5.1.0), cluster(v2.1.6), sp(2.1-4), spdep(v1.3-5), igraph(v1.6.0), circlize(v0.4.15), ComplexHeatmap (v2.15.4), ggsci(v3.0.3), grid(v4.1.2), psych(v2.4.3), RColorBrewer(v1.1-3), ggplot2(v3.5.0), viridis(v0.6.5), tidytext(v0.4.1), dendextend(v1.17.1), anndata(v0.7.5.6), reticulate(v1.40.0).
• Python (v3.12.2, only for Tutorial 3).
• Python packages: Scanpy (v1.11.0), Palantir (v1.3.3)