sincei: A toolkit for QC, counting, clustering and plotting of single-cell (epi)genomics data on a terminal.
sincei provides a flexible, easy-to-use command-line interface to work with single-cell data directly from BAM files. It can:
- Aggregate signal in bins, genes or any feature of interest from single-cells.
- Perform read-level and count-level quality control.
- Perform dimensionality reduction and clustering of all kinds of single-cell data (open chromatin, histone marks, methylation, gene expression etc..).
- Create coverage files (bigwigs) for visualization.
- Along with additional tools for visualization, interpretation/annotation of cells.
sincei is also part of the scVerse ecosystem, and it's command-line tools can easily with combined with various Python or R packages for further analysis.
For details, please read our preprint describing sincei.
Please browse the full documentation for tutorials on how to use sincei on the command line, as well as details of our python API.
sincei is a command line toolkit based on python3, and can be installed using conda.
The recommended way to install sincei is via bioconda:
conda create -n sincei -c bioconda -c conda-forge sincei
Alternatively, a development version can be installed via GitHub.
conda create -n sincei -c conda-forge python=3.12
conda activate sincei
pip install --editable=git+https://github.com/bhardwaj-lab/sincei.git@develop#egg=sincei
For the daring and impatient, we have an upcoming release of sincei with significantly faster tools thanks to the Rust backend, currently under testing. We encourage you to install sincei from the rust_release branch and give it a try. If you encounter any errors, please let us know by opening an issue and continue using the stable version from pypi/bioconda. Thanks!
Get the tool list with sincei --help
Each tool begins with the prefix sc<tool_name>, such as:
scBulkCoverage -b file1.bam -g groupinfo.txt -o coverage
Please cite sincei as: "Bhardwaj V. , Mourragui, S. (2024) User-friendly exploration of epigenomic data in single cells using sincei. biorXiv. doi: 10.1101/2024.07.27.605424"
To ask a question related to sincei or start a new discussion, please use our github discussion forum.