============ Cellsnp-lite

|conda| |platforms| |license|

.. |conda| image:: https://anaconda.org/bioconda/cellsnp-lite/badges/version.svg :target: https://bioconda.github.io/recipes/cellsnp-lite/README.html .. |platforms| image:: https://anaconda.org/bioconda/cellsnp-lite/badges/platforms.svg :target: https://bioconda.github.io/recipes/cellsnp-lite/README.html .. |license| image:: https://anaconda.org/bioconda/cellsnp-lite/badges/license.svg :target: https://bioconda.github.io/recipes/cellsnp-lite/README.html

Cellsnp-lite: Efficient Genotyping Bi-Allelic SNPs on Single Cells

Cellsnp-lite is a C/C++ tool for efficient genotyping bi-allelic SNPs on single cells. You can use cellsnp-lite after read alignment to obtain the snp x cell pileup UMI or read count matrices for each alleles of given or detected SNPs.

The output from cellsnp-lite can be directly used for downstream analysis such as:

#. Donor deconvolution in multiplexed single-cell RNA-seq data (e.g., with vireo_). #. Allele-specific CNV analysis in single-cell or spatial transcriptomics data (e.g., with Numbat_ or XClone_). #. Clonal substructure discovery using single cell mitochondrial variants (e.g., with MQuad_).

Cellsnp-lite has following features:

• Wide applicability: cellsnp-lite can take data from various omics as input, including RNA-seq, DNA-seq, ATAC-seq, either in bulk or single cells.
• Simplified user interface that supports parallel computing, cell barcode and UMI tags.
• High efficiency in terms of running speed and memory usage, with highly concordant results compared to existing methods.

For details of the tool, please checkout our paper:

Xianjie Huang, Yuanhua Huang, Cellsnp-lite: an efficient tool for 
genotyping single cells, 
Bioinformatics, Volume 37, Issue 23, December 2021, Pages 4569–4571, 
https://doi.org/10.1093/bioinformatics/btab358

Installation

Cellsnp-lite depends on several external libraries such as htslib_. We highly recommend installing cellsnp-lite via conda_ to avoid potential issues regarding dependency.

.. code-block:: bash

conda install -c bioconda cellsnp-lite

Alternatively, you may also compile from source code. For details, please check install from this github repo_ in the user guide.

Manual

The full manual is available in the user guide at https://cellsnp-lite.readthedocs.io

FAQ and feedback

For troubleshooting, please have a look of FAQ.rst, and we welcome reporting any issue for bugs, questions and new feature requests.

Acknowledgement

Cellsnp-lite heavily depends on htslib_ for accessing high-throughput sequencing data. In addition, it uses the kvec.h file (from klib_) for dynamic array usage and the thpool.{h,c} files (from C-Thread-Pool_) for thread pool management.

.. _C-Thread-Pool: https://github.com/Pithikos/C-Thread-Pool .. _conda: https://docs.conda.io/en/latest/ .. _FAQ.rst: https://github.com/single-cell-genetics/cellsnp-lite/blob/master/docs/main/FAQ.rst .. _htslib: https://github.com/samtools/htslib .. _install from this github repo: https://cellsnp-lite.readthedocs.io/en/latest/install.html#install-from-this-github-repo-latest-stable-dev-version .. _issue: https://github.com/single-cell-genetics/cellsnp-lite/issues .. _klib: https://github.com/attractivechaos/klib .. _MQuad: https://github.com/single-cell-genetics/MQuad .. _Numbat: https://github.com/kharchenkolab/numbat .. _vireo: https://github.com/huangyh09/vireo .. _XClone: https://github.com/single-cell-genetics/XClone