cellsnp-lite aims to pileup the expressed alleles in single-cell or bulk RNA-seq data, which can be directly used for donor deconvolution in multiplexed single-cell RNA-seq data, particularly with vireo, which assigns cells to donors and detects doublets, even without genotyping reference.

cellsnp-lite heavily depends on htslib. This program should give very similar results as samtools/bcftools mpileup. Also, there are two major differences comparing to bcftools mpileup:

1. cellsnp-lite can now pileup a list of positions, with directly splitting into a list of cell barcodes, e.g., for 10x genome. With bcftools, you may need to manipulate the RG tag in the bam file if you want to divide reads into cell barcode groups.
2. cellsnp-lite uses simple filtering for outputting SNPs, i.e., total UMIs or counts and minor alleles fractions. The idea here is to keep most information of SNPs and the downstream statistical model can take the full use of it.

cellsnp-lite is the C version of cellSNP, which is implemented in Python. Compared to cellSNP, cellsnp-lite is basically more efficient with higher speed and less memory usage.

Mode 2 is now avaliable in cellsnp-lite. cellsnp-lite mode 2 is aimed to pileup whole chromosome(s) for a single BAM/SAM file.

The command line option --maxFLAG is now deprecated, please use --inclFLAG and --exclFLAG instead, which are more flexible for reads filtering.

All release notes can be found in doc/release.rst.

For computational efficiency, we initialised comments on this: doc/speed.rst

cellsnp-lite is implemented in C. You can install it via conda or from this github repo.

• Method 1: Install via conda (latest stable version)

Step 1: add config

conda config --add channels bioconda
conda config --add channels conda-forge

Step 2: install

to your current environment:

conda install cellsnp-lite

or to a new environment:

conda create -n CSP cellsnp-lite     # you can replace 'CSP' with another env name.

• Method 2: Install from this Github Repo (latest stable/dev version)

cellsnp-lite depends on zlib and htslib. The two libs should have been installed in the system before installing cellsnp-lite. Then to install cellsnp-lite,

git clone https://github.com/single-cell-genetics/cellsnp-lite.git;
cd cellsnp-lite;
make;
sudo make install;

By default, this will build against an HTSlib source tree in ../htslib. You can alter this to a source tree elsewhere or to a previously-installed HTSlib by running make htslib_dir=<path_to_htslib_dir>.

Besides, if you met the error error while loading shared libraries: libhts.so.3 when running cellsnp-lite, you could fix this by setting environment variable LD_LIBRARY_PATH to proper value,

echo 'export LD_LIBRARY_PATH=<abspath_to_htslib_dir>:$LD_LIBRARY_PATH' >> ~/.bashrc;
source ~/.bashrc;

Once installed, check all arguments by type cellsnp-lite -h (see a snapshot) There are three modes of cellsnp-lite:

• Mode 1: pileup a list of SNPs for a single BAM/SAM file

Use both -R and -b.

Require: a single BAM/SAM file, e.g., from cellranger, a list of cell barcodes, a VCF file for common SNPs. This mode is recommended comparing to mode 2, if a list of common SNP is known, e.g., human (see Candidate SNPs below)

cellsnp-lite -s $BAM -b $BARCODE -O $OUT_DIR -R $REGION_VCF -p 20 --minMAF 0.1 --minCOUNT 20 --gzip

As shown in the above command line, we recommend filtering SNPs with <20UMIs or <10% minor alleles for downstream donor deconvolution, by adding --minMAF 0.1 --minCOUNT 20

Besides, special care needs to be taken when filtering PCR duplicates for scRNA-seq data by setting maxFLAG to a small value, for the upstream pipeline may mark each extra read sharing the same CB/UMI pair as PCR duplicate, which will result in most variant data being lost. Due to the reason above, cellsnp-lite by default uses a non-DUP exclFLAG value to include PCR duplicates for scRNA-seq data when UMItag is turned on.

• Mode 2: pileup whole chromosome(s) for a single BAM/SAM file

Don't use -R but flexible on -b.

This mode requires inputting a single bam file with either cell barcoded (add -b) or a bulk sample:

# 10x sample with cell barcodes
cellsnp-lite -s $BAM -b $BARCODE -O $OUT_DIR -p 22 --minMAF 0.1 --minCOUNT 100 --gzip

# a bulk sample without cell barcodes and UMI tag
cellsnp-lite -s $bulkBAM -I Sample0 -O $OUT_DIR -p 22 --minMAF 0.1 --minCOUNT 100 --UMItag None --gzip

Add --chrom if you only want to genotype specific chromosomes, e.g., 1,2, or chrMT.

Recommend filtering SNPs with <100UMIs or <10% minor alleles for saving space and speed up inference when pileup whole genome: --minMAF 0.1 --minCOUNT 100.

Note, this mode may output false positive SNPs, for example somatic variants or falses caussed by RNA editing. These false SNPs are probably not consistent in all cells within one individual, hence confounding the demultiplexing. Nevertheless, for species, e.g., zebrafish, without a good list of common SNPs, this strategy is still worth a good try, and it does not take much more time than mode 1.

• Mode 3: pileup a list of SNPs for one or multiple BAM/SAM files

Use -R but not -b.

Require: one or multiple BAM/SAM files (bulk or smart-seq), their according sample ids (optional), and a VCF file for a list of common SNPs. BAM/SAM files can be input in comma separated way (-s) or in a list file (-S).

cellsnp-lite -s $BAM1,$BAM2,$BAM3 -I sample_id1,sample_id2,sample_id3 -O $OUT_DIR -R $REGION_VCF -p 20 --UMItag None --gzip

cellsnp-lite -S $BAM_list_file -I sample_list_file -O $OUT_DIR -R $REGION_VCF -p 20 --UMItag None --gzip

Set filtering thresholds according to the downstream analysis. Please add --UMItag None if your bam file does not have UMIs, e.g., smart-seq and bulk RNA-seq.

The command line option --maxFLAG is now deprecated, please use --inclFLAG and --exclFLAG instead, which are more flexible for reads filtering. You could refer to the explain_flags page to easily aggregate and convert all flag bits into one integer. One example is that the default exclFLAG value (without using UMIs) is 1796, which is calculated by adding four flag bits: UNMAP (4), SECONDARY (256), QCFAIL (512) and DUP (1024).

A quality list of candidate SNPs (ususally common SNPs) are important for mode 1 and mode 3. If a list of genotyped SNPs is available, it can be used to pile up. Alternatively, for human, common SNPs in population that have been idenetified from consortiums can also be very good candidates, e.g., gnomAD and 1000_Genome_Project. For the latter, we have compiled a list of 7.4 million common variants (AF>5%) with this bash script and stored in this folder.

In case you want to lift over SNP positions in vcf file from one genome build to another, see our LiftOver_vcf wrap function.

For troubleshooting, please have a look of FAQ.rst, and we welcome reporting any issue.