https://github.com/cellgeni/nf-cellsnplite
https://github.com/cellgeni/nf-cellsnplite
Last synced: 4 months ago
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- Host: GitHub
- URL: https://github.com/cellgeni/nf-cellsnplite
- Owner: cellgeni
- Created: 2025-04-29T10:48:35.000Z (about 1 year ago)
- Default Branch: main
- Last Pushed: 2025-06-24T14:29:37.000Z (11 months ago)
- Last Synced: 2025-09-10T04:57:31.441Z (9 months ago)
- Language: Nextflow
- Size: 394 KB
- Stars: 0
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# nf-cellsnplite
nf-cellsnplite is the Nextflow implementation of [cellsnp-lite package](https://cellsnp-lite.readthedocs.io/en/latest/index.html) introduced in [Huang et al, 2021](https://doi.org/10.1093/bioinformatics/btab358).
## How to run:
The recommended way to use nextflow is to run it in a screen session. These steps can be directly used in Sanger's FARM, but you can modify each step according to the environment you're working on or the job scheduler your HPC uses:
1. Start a screen session: `screen -S nf_run1`
2. Start a small interactive job for nextflow: `bsub -G cellgeni -n1 -R"span[hosts=1]" -Is -q long -R"select[mem>2000] rusage[mem=2000]" -M2000 bash`
3. Modify one of RESUME scripts in examples folder (pre-made Nextflow run scripts)
4. Run the RESUME scripts you modified: `./RESUME-scautoqc-all`
5. You can leave your screen session and let it run in the background: `Ctrl+A, D`
6. You can go back to the screen session by: `screen -x nf_run1`
## Files:
* `main.nf` - the Nextflow pipeline that executes scAutoQC pipeline.
* `nextflow.config` - the configuration script that allows the processes to be submitted to IBM LSF on Sanger's HPC and ensures correct environment is set via singularity container (this is an absolute path). Global default parameters are also set in this file and some contain absolute paths.
* `examples/` - a folder that includes pre-made Nextflow run scripts for each workflow:
* `RESUME-cellsnplite-2step`
* `RESUME-cellsnplite-1step` (joint calling + genotyping, therefore slower)
* `Dockerfile` - a dockerfile to reproduce the environment used to run the pipeline.
## Pipeline Arguments
* `--SAMPLEFILE` - The path to the sample file provided to the pipeline. This is a tab-separated file with one sample per line. Each line should contain a sample id, path to BAM file, and path to path to barcodes file (in that order!).
* `--minMAF` - Minimum minor allele frequency, which is the minimum frequency of the allele with second highest read or UMI count for a given SNP site. Default is 0.
* `--minCOUNT` - Minimum aggregated UMI or read count. Default is 20.
* `--genotype` - If use, do genotyping in addition to counting. By default, cellsnp-lite would not output the file `cellSNP.cells.vcf` which contains the genotypes (e.g., “0/0”, “1/0”, “1/1”) in single cells.
* `--project_tag` - Project name to specify the run to add to the end of output folder (e.g. cellsnplite-results-test1)
* `--UMItag` - Tag for UMI: one of UB, Auto, None. Default is Auto for the tool, and UB for the nextflow pipeline
## Profiles
This pipeline supports multiple configuration profiles (e.g., `rna`, `atac`) defined in `nextflow.config`.
To select a profile, use the `-profile` option with the `nextflow run` command:
```
nextflow run cellgeni/nf-cellsnplite -r main -profile rna
```
or
```
nextflow run cellgeni/nf-cellsnplite -r main -profile atac
```
You should specify a profile, otherwise it will fail.
## Workflow
This pipeline produces 3 types of outputs, each were detailed in their corresponding steps:
* ***[output 1]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation (without GT) `cellSNP.base.vcf.gz`
* ***[output 2]:*** A SNP x cell sparse matrix in “Matrix Market exchange formats”, containing the allele depths of the alternative (ALT) alleles, the sum of allele depths of the reference and alternative alleles (REF+ALT) and the sum of allele depths of all the alleles other than REF and ALT. `cellSNP.tag.(AD/DP/OTH).mtx`
* ***[output 3]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation per cell `cellSNP.cells.vcf.gz`
The default version of the pipeline runs 2-step approach as shown the diagram above. The other workflow (1-step approach) is more useful for smaller datasets since it involves both joint calling and genotyping, and therefore is substantially slower than 2-step approach, and is not suggested to use for generic scRNA-seq datasets
* `2step`: runs 2b-1a steps
* `1step`: runs 2a step
* `1a-only`: runs the second step of the `2step` worklflow only (1a)
### 1. `2b`
This step genotypes the 10x sample in a pseudo-bulk manner without given SNPs.
This step requires a single input:
* BAM file from Cell Ranger or STARsolo
This step produces:
* * ***[output 1]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation (without GT) `cellSNP.base.vcf.gz`
* ***[output 2]:*** A SNP x cell sparse matrix in “Matrix Market exchange formats”, containing the allele depths of the alternative (ALT) alleles, the sum of allele depths of the reference and alternative alleles (REF+ALT) and the sum of allele depths of all the alleles other than REF and ALT. `cellSNP.tag.(AD/DP/OTH).mtx`
### 2. `1a`
This step genotypes single cells at a list of given SNPs (called heterouzygous variants in previous step 2b).
This step produces:
* ***[output 1]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation (without GT) `cellSNP.base.vcf.gz`
* ***[output 2]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation per cell `cellSNP.cells.vcf.gz`
* ***[output 3]:*** A SNP x cell sparse matrix in “Matrix Market exchange formats”, containing the allele depths of the alternative (ALT) alleles, the sum of allele depths of the reference and alternative alleles (REF+ALT) and the sum of allele depths of all the alleles other than REF and ALT `cellSNP.tag.(AD/DP/OTH).mtx`
### 3. `2a`
This step genotypes the single cells without given SNPs. It does joint calling and genotyping, therefore it is substantially slower than 2-step approach, and it is only useful for smaller datasets, or for small chromosomes (mitochondrial).
This step produces:
* ***[output 1]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation (without GT) `cellSNP.base.vcf.gz`
* ***[output 2]:*** A VCF file listing genotyped SNPs and aggregated AD & DP infomation per cell `cellSNP.cells.vcf.gz`
* ***[output 3]:*** A SNP x cell sparse matrix in “Matrix Market exchange formats”, containing the allele depths of the alternative (ALT) alleles, the sum of allele depths of the reference and alternative alleles (REF+ALT) and the sum of allele depths of all the alleles other than REF and ALT `cellSNP.tag.(AD/DP/OTH).mtx`