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
Cellsnp-lite depends on several external libraries such as htslib. We highly recommend installing cellsnp-lite via conda to avoid potential issues regarding dependency.
conda install -c bioconda cellsnp-liteAlternatively, you may also compile from source code. For details, please check install from this github repo in the user guide.
The full manual is available in the user guide at https://cellsnp-lite.readthedocs.io
For troubleshooting, please have a look of FAQ.rst, and we welcome reporting any issue for bugs, questions and new feature requests.
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.