mitoClone2

The R package is used for performing the analysis of clonal heterogeneity based on nuclear and mitochondrial mutations in single cell RNA or DNA sequencing.

You can read about the package and explore an extensive vignette in the supplemental section of our accompanying publication:

Story B, Velten L, Mönke G, Annan A, Steinmetz L. Mitoclone2: an R package for elucidating clonal structure in single-cell RNA-sequencing data using mitochondrial variants. NAR Genomics and Bioinformatics. 2024;6(3):lqae095. Published 2024 Aug 9. doi:10.1093/nargab/lqae095

1. System Requirements:

• Linux/Mac
• R 4.0+
• SCITE/PhISCS (included)
• Python 2.7, 3.6, or 3.7 (optional)

Importantly, depending on the user's need for tree-building, an installation of PhiSCS may be necessary. For SCITE, the program should be installed automatically when the mitoClone2 package is installed. Please read the manual provided by the software authors SCITE Installation Instructions to better understand the software.

See DESCRIPTION file for specific R package requirements.

The software has been successfully implemented and tested using: Python 3.6.5, R 4.0.0, and on Ubuntu 18.04, CentOS 7, macOS 14.5, and Windows 11.

2. Installation

For manual package installation use the command:

## install via Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install('mitoClone2')
## use devtools to install
##devtools::install_github("benstory/mitoClone2")

Estimated installation time: < 1 hour*

3. Demo

Please see R vignettes for further instructions and a demo using real data. Use the command vignette("mitoClone2") after loading the library (see Instructions) to list all available tutorials.

Estimated demo completion time: < 1 hour

4. Usage Instructions

After installing all dependencies, open an R session and load the library using the following command:

library(mitoClone2)

Notes: Please make sure to set your environmental python variables correctly for use of gurobi. See for example the python_env parameter.

Again please view the R vignettes for usage possibilities.

• overview: Instructions on how to filter mitochondrial mutations using either a list of sites to be excluded or shared mutations across samples/patients (typical runtime: > 10 minutes)
• clustering: Instructions on how to cluster mutations into a clonal hierarchy and how to assign cells to clones (typical runtime: < 1 hour)