The typical command for running the pipeline is as follows:
nextflow run qbic-pipelines/cellranger --input 'samplesheet.tsv' --genome 'GRCh38' -profile docker
This will launch the pipeline with the docker
configuration profile. See below for more information about profiles.
Note that the pipeline will create the following files in your working directory:
work # Directory containing the nextflow working files
results # Finished results (configurable, see below)
.nextflow_log # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.
When you run the above command, Nextflow automatically pulls the pipeline code from GitHub and stores it as a cached version. When running the pipeline after this, it will always use the cached version if available - even if the pipeline has been updated since. To make sure that you're running the latest version of the pipeline, make sure that you regularly update the cached version of the pipeline:
nextflow pull qbic-pipelines/cellranger
It's a good idea to specify a pipeline version when running the pipeline on your data. This ensures that a specific version of the pipeline code and software are used when you run your pipeline. If you keep using the same tag, you'll be running the same version of the pipeline, even if there have been changes to the code since.
First, go to the qbic-pipelines/cellranger releases page and find the latest version number - numeric only (eg. 1.3.1
). Then specify this when running the pipeline with -r
(one hyphen) - eg. -r 1.3.1
.
This version number will be logged in reports when you run the pipeline, so that you'll know what you used when you look back in the future.
A sample sheet containing sample metadata and paths to the input fastq files is needed to run the pipeline and should be provided with the --input
parameter.
Example sample sheet:
GEM Sample Lane R1 R2 I1
GEM1 pbmc1_1k_v3_mini L001 pbmc1_1k_v3_mini_S1_L001_R1_001.fastq.gz pbmc1_1k_v3_mini_S1_L001_R2_001.fastq.gz pbmc1_1k_v3_mini_S1_L001_I1_001.fastq.gz
GEM1 pbmc1_1k_v3_mini L002 pbmc1_1k_v3_mini_S1_L002_R1_001.fastq.gz pbmc1_1k_v3_mini_S1_L002_R2_001.fastq.gz pbmc1_1k_v3_mini_S1_L002_I1_001.fastq.gz
GEM2 pbmc2_1k_v3_mini L001 pbmc2_1k_v3_mini_S1_L002_R1_001.fastq.gz pbmc2_1k_v3_mini_S1_L002_R2_001.fastq.gz pbmc2_1k_v3_mini_S1_L002_I1_001.fastq.gz
- GEM: GEM ID. If samples were pooled in the same GEM, but sequenced in different lanes, then they will be processed together with
cellranger count
. See this info to know more about this. - Sample: Sample ID.
- Lane: Lane ID. If Fastq files have the same sample ID and different lane IDs, they will be merged automatically by
cellranger count
. - R1: First mate (R1) of fastq files.
- R2: Second mate (R2) of fastq files.
- I1: Index file (optional). If index file is provided, please set the flag
--index_file true
.
Genome reference data can be provided in several ways.
The reference data provided by 10x genomics is automatically downloaded in this pipeline. Only human (GRCh38
) and mouse references (mm10
) are supported. Please set the following:
- Human data:
--genome GRCh38
- Mouse data:
--genome mm10
If you want to save these references for use in other analysis, you can provide the parameter --save_references
. Then in your next run, the path to the saved references can be provided with the --reference
parameter.
To build custom references as part of the pipeline, you can provide a fasta and a gtf file (best is from Ensembl):
--gtf gtf_file.gtf --fasta genome_fasta.fa --reference_name my_custom_reference
If you have already a pre-built reference folder, it can also be directly provided:
--prebuilt_reference reference
NB: These options are part of Nextflow and use a single hyphen (pipeline parameters use a double-hyphen).
Use this parameter to choose a configuration profile. Profiles can give configuration presets for different compute environments.
Several generic profiles are bundled with the pipeline which instruct the pipeline to use software packaged using different methods (Docker, Singularity, Podman, Shifter, Charliecloud, Conda) - see below.
We highly recommend the use of Docker or Singularity containers for full pipeline reproducibility, however when this is not possible, Conda is also supported.
The pipeline also dynamically loads configurations from https://github.com/nf-core/configs when it runs, making multiple config profiles for various institutional clusters available at run time. For more information and to see if your system is available in these configs please see the nf-core/configs documentation.
Note that multiple profiles can be loaded, for example: -profile test,docker
- the order of arguments is important!
They are loaded in sequence, so later profiles can overwrite earlier profiles.
If -profile
is not specified, the pipeline will run locally and expect all software to be installed and available on the PATH
. This is not recommended.
docker
- A generic configuration profile to be used with Docker
- Pulls software from Docker Hub:
qbicpipelines/cellranger
singularity
- A generic configuration profile to be used with Singularity
- Pulls software from Docker Hub:
qbicpipelines/cellranger
podman
- A generic configuration profile to be used with Podman
- Pulls software from Docker Hub:
qbicpipelines/cellranger
shifter
- A generic configuration profile to be used with Shifter
- Pulls software from Docker Hub:
qbicpipelines/cellranger
charliecloud
- A generic configuration profile to be used with Charliecloud
- Pulls software from Docker Hub:
qbicpipelines/cellranger
conda
test
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
Specify this when restarting a pipeline. Nextflow will used cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously.
You can also supply a run name to resume a specific run: -resume [run-name]
. Use the nextflow log
command to show previous run names.
Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.
Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the steps in the pipeline, if the job exits with an error code of 143
(exceeded requested resources) it will automatically resubmit with higher requests (2 x original, then 3 x original). If it still fails after three times then the pipeline is stopped.
Whilst these default requirements will hopefully work for most people with most data, you may find that you want to customise the compute resources that the pipeline requests. You can do this by creating a custom config file. For example, to give the workflow process star
32GB of memory, you could use the following config:
process {
withName: star {
memory = 32.GB
}
}
To find the exact name of a process you wish to modify the compute resources, check the live-status of a nextflow run displayed on your terminal or check the nextflow error for a line like so: Error executing process > 'bwa'
. In this case the name to specify in the custom config file is bwa
.
See the main Nextflow documentation for more information.
If you are likely to be running nf-core
pipelines regularly it may be a good idea to request that your custom config file is uploaded to the nf-core/configs
git repository. Before you do this please can you test that the config file works with your pipeline of choice using the -c
parameter (see definition above). You can then create a pull request to the nf-core/configs
repository with the addition of your config file, associated documentation file (see examples in nf-core/configs/docs
), and amending nfcore_custom.config
to include your custom profile.
If you have any questions or issues please send us a message on Slack on the #configs
channel.
Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.
The Nextflow -bg
flag launches Nextflow in the background, detached from your terminal so that the workflow does not stop if you log out of your session. The logs are saved to a file.
Alternatively, you can use screen
/ tmux
or similar tool to create a detached session which you can log back into at a later time.
Some HPC setups also allow you to run nextflow within a cluster job submitted your job scheduler (from where it submits more jobs).
In some cases, the Nextflow Java virtual machines can start to request a large amount of memory.
We recommend adding the following line to your environment to limit this (typically in ~/.bashrc
or ~./bash_profile
):
NXF_OPTS='-Xms1g -Xmx4g'