Usage

1. Basic run

This pipeline fetches Expression Atlas and GEO accessions for the provided species and downloads the corresponding data.

nextflow run nf-core/stableexpression \
   -profile <PROFILE (examples: docker / apptainer / conda / micromamba)> \
   --species <SPECIES (examples: arabidopsis_thaliana / "drosophila melanogaster")> \
   --outdir <OUTDIR (example: ./results)>

2. Specific public datasets

You can provide keywords to restrict downloaded datasets to specific conditions.

nextflow run nf-core/stableexpression \
   -profile <PROFILE> \
   --species <SPECIES> \
   --keywords <KEYWORDS (examples: "leaf" / "flower,stress")>
   --outdir <OUTDIR>

3. Provide your own accessions

You may already have an idea of specific Expression Atlas / GEO accessions you want to use in the analysis. In this case, you can provide them directly to the pipeline.

nextflow run nf-core/stableexpression \
   -profile <PROFILE> \
   --species <SPECIES> \
   --skip_fetch_eatlas_accessions \
   [--eatlas_accessions <ACCESSION(S) (example: "E-MTAB-7711,E-GEOD-51720")>] \
   [--eatlas_accessions_file <FILE>] \
   [--geo_accessions <ACCESSION(S) (example: "GSE262492,GSE305365")>] \
   [--geo_accessions_file <FILE>] \
   --outdir <OUTDIR>

In case you do not know which accessions you want but you would like to control precisely which datasets are included in you analysis, you may run first:

nextflow run nf-core/stableexpression \
   -profile <PROFILE> \
   --species <SPECIES> \
   --accessions_only \
   --outdir <OUTDIR>

Fetched accessions with their respective metadata will be available in <OUTDIR>/expression_atlas/accessions/ and <OUTDIR>/geo/accessions/

4. Use your own expression datasets

You can of course provide your own counts datasets / experimental designs.

First, prepare a CSV samplesheet listing the different count datasets you want to use. Each row represents a specific dataset and must contain:

It should look as follows:

counts,design,platform,normalised
path/to/normalised.counts.csv,path/to/normalised.design.csv,rnaseq,true
path/to/raw.counts.csv,path/to/raw.design.csv,rnaseq,false
path/to/microarray.counts.csv,path/to/microarray.design.csv,microarray,true

It can also be a YAML file:

- counts: path/to/normalised.counts.csv
  design: path/to/normalised.design.csv
  platform: rnaseq
  normalised: true
- counts: path/to/raw.counts.csv
  design: path/to/raw.design.csv
  platform: rnaseq
  normalised: false
- counts: path/to/microarray.counts.csv
  design: path/to/microarray.design.csv
  platform: microarray
  normalised: true

The counts should have the following structure:

gene_id,sample_A,sample_B,sample_C
gene_1,1,2,3
gene_2,1,2,3

While the design should look like:

sample,condition
sample_A,condition_1
sample_B,condition_2
sample_C,condition_1

Now run the pipeline with:

nextflow run nf-core/stableexpression \
   -profile <PROFILE> \
   --species <SPECIES> \
   --datasets <CSV / YAML FILE> \
   --skip_fetch_eatlas_accessions \
   --outdir <OUTDIR>

5. Custom gene ID mapping / metadata / length

You can supply your own:

• gene id mapping file
• gene metadata file
• gene length file

The gene ID mapping file is used to map gene IDs in count table(s) (local or downloaded) to more generic IDs that will be used as basis fore subsequent steps.

The gene metadata file provides additional information about the genes, such as their common name and description.

The gene length file provides the length of each gene, which is used to compute the TPM values during gene expression normalisation.

nextflow run nf-core/stableexpression \
   -profile <PROFILE> \
   --species <SPECIES> \
   --datasets <CSV / YAML FILE> \
   --gene_id_mapping <CSV FILE> \
   --gene_metadata <CSV FILE> \
   --gene_length <CSV FILE> \
   --skip_fetch_eatlas_accessions \
   --outdir <OUTDIR>

Structure of the gene id mapping file:

Example:

original_gene_id,gene_id
gene_A,ENSG1234567890
geneB,OTHERmappedgeneID

Structure of the gene metadata file:

Example:

gene_id,name,description
ENSG1234567890,Gene A,Description of gene A
OTHERmappedgeneID,My OTHER Gene,Another description

Structure of the gene length file:

Example:

gene_id,length
ENSG1234567890,1000
OTHERmappedgeneID,2000
 
### 6. More advanced scenarios
 
For advanced scenarios, you can see the list of available parameters in the [parameter documentation](https://nf-co.re/stableexpression/parameters).
 
## Pipeline output
 
Note that the pipeline will create the following files in your working directory:
 
```bash
work                # Directory containing the nextflow working files
<OUTDIR>            # Finished results in specified location (defined with --outdir)
.nextflow_log       # Log file from Nextflow
# Other nextflow hidden files, eg. history of pipeline runs and old logs.

For a detailed description of the output files, please consult the nf-core stableexpression output directory structure.

Parameters

If you wish to repeatedly use the same parameters for multiple runs, rather than specifying each flag in the command, you can specify these in a params file.

Pipeline settings can be provided in a yaml or json file via -params-file <file>.

The above pipeline run specified with a params file in yaml format:

nextflow run -r dev nf-core/stableexpression -profile docker -params-file params.yaml

with:

species: 'Homo sapiens'
datasets: './datasets.csv'
outdir: './results/'
<...>

You can also generate such YAML/JSON files via nf-core/launch.

Updating the pipeline

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 nf-core/stableexpression

Reproducibility

It is a good idea to specify the 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 nf-core/stableexpression releases page and find the latest pipeline version - numeric only (eg. 1.3.1). Then specify this when running the pipeline with -r (one hyphen) - eg. -r 1.3.1. Of course, you can switch to another version by changing the number after the -r flag.

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. For example, at the bottom of the MultiQC reports.

To further assist in reproducibility, you can use share and reuse parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.

Core Nextflow arguments

-profile

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, Apptainer, Conda) - see below.

When running the pipeline of multi-user server or on a cluster, the best practice is to use Apptainer (formerly Singularity). You can install Apptainer by following these instructions. In case you encounter the following error when running Apptainer:

ERROR  : Could not write info to setgroups: Permission denied
ERROR  : Error while waiting event for user namespace mappings: no event received

you may need to install the apptainer-suid package instead of apptainer:

# Debian / Ubuntu
sudo apt install apptainer-suid
# RHEL / CentOS
sudo yum install apptainer-suid
# Fedora
sudo dnf install apptainer-suid

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 check if your system is supported, 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, since it can lead to different results on different machines dependent on the computer environment.

• test
  • A profile with a complete configuration for automated testing
  • Includes links to test data so needs no other parameters
• apptainer
  • A generic configuration profile to be used with Apptainer
• docker
  • A generic configuration profile to be used with Docker
• singularity
  • A generic configuration profile to be used with Singularity
• podman
  • A generic configuration profile to be used with Podman
• shifter
  • A generic configuration profile to be used with Shifter
• charliecloud
  • A generic configuration profile to be used with Charliecloud
• wave
  • A generic configuration profile to enable Wave containers. Use together with one of the above (requires Nextflow 24.03.0-edge or later).
• conda
  • A generic configuration profile to be used with Conda. Please only use Conda as a last resort i.e. when it’s not possible to run the pipeline with Docker, Singularity, Podman, Shifter, Charliecloud, or Apptainer.
• micromamba
  • A faster, more lightweight alternative to Conda. As for Conda, use Micromamba as a last resort.

-resume

Specify this when restarting a pipeline. Nextflow will use cached results from any pipeline steps where the inputs are the same, continuing from where it got to previously. For input to be considered the same, not only the names must be identical but the files’ contents as well. For more info about this parameter, see this blog post.

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.

-c

Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.

Custom configuration

Resource requests

Whilst the default requirements set within the pipeline will hopefully work for most people and with most input data, you may find that you want to customise the compute resources that the pipeline requests. Each step in the pipeline has a default set of requirements for number of CPUs, memory and time. For most of the pipeline steps, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher resources request (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.

To change the resource requests, please see the max resources and tuning workflow resources section of the nf-core website.

Custom Containers

In some cases, you may wish to change the container or conda environment used by a pipeline steps for a particular tool. By default, nf-core pipelines use containers and software from the biocontainers or bioconda projects. However, in some cases the pipeline specified version maybe out of date.

To use a different container from the default container or conda environment specified in a pipeline, please see the updating tool versions section of the nf-core website.

Custom Tool Arguments

A pipeline might not always support every possible argument or option of a particular tool used in pipeline. Fortunately, nf-core pipelines provide some freedom to users to insert additional parameters that the pipeline does not include by default.

To learn how to provide additional arguments to a particular tool of the pipeline, please see the customising tool arguments section of the nf-core website.

nf-core/configs

In most cases, you will only need to create a custom config as a one-off but if you and others within your organisation are likely to be running nf-core pipelines regularly and need to use the same settings 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. 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.

See the main Nextflow documentation for more information about creating your own configuration files.

If you have any questions or issues please send us a message on Slack on the #configs channel.

Running in the background

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).

Nextflow memory requirements

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'