An analysis pipeline for Nanostring nCounter expression data.
The *nf-core/nanostring* pipeline allows the analysis of NanoString data. The pipeline performs quality control, normalization and annotation of the obtained counts.
You will need to create a samplesheet with information about the samples you would like to analyse before running the pipeline. Use this parameter to specify its location. It has to be a comma-separated file with 3 columns, and a header row as shown in the examples below.
--input '[path to samplesheet file]'
Multiple runs of the same sample
sample identifiers should be the same when you measured the same sample multiple times. Below is an example for the same sample measured twice:
RCC_FILE,SAMPLE_ID /path/to/sample1.RCC,sample1 /path/to/sample2_1.RCC,sample2 /path/to/sample2_2.RCC,sample2
The samplesheet can have as many columns as you desire, however, there is a strict requirement for the two columns
A final samplesheet with additional metadata may look something like the one below. This is for 3 samples. If you want to use additional metadata columns please follow the instructions provided in the section on Gene-Count Heatmap.
If the column
RCC_FILE_NAME is not specified, the pipeline will fill it automatically from the
RCC_FILE,RCC_FILE_NAME,SAMPLE_ID,TIME,TREATMENT,INCLUDE,OTHER_METADATA /path/to/sample1.RCC,sample1.RCC,sample1,1,0,1,your metadata /path/to/sample2_1.RCC,sample2_1.RCC,sample2,2,0,1,your metadata /path/to/sample2_2.RCC,sample2_2.RCC,sample2,2,0,1,your metadata
| Column | Description |
| ----------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
SAMPLE_ID | Custom sample name. This entry will be identical for multiple measurements. Spaces in sample names are automatically converted to underscores (
RCC_FILE | Full path to RCC file of NanoString measurement. |
An example samplesheet has been provided with the pipeline.
Running the pipeline
The typical command for running the pipeline is as follows:
nextflow run nf-core/nanostring --input samplesheet.csv --outdir <OUTDIR> -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 <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.
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
json file via
⚠️ Do not use
-c <file>to specify parameters as this will result in errors. Custom config files specified with
-cmust only be used for tuning process resource specifications, other infrastructural tweaks (such as output directories), or module arguments (args).
The above pipeline run specified with a params file in yaml format:
nextflow run nf-core/nanostring -profile docker -params-file params.yaml
input: './samplesheet.csv' outdir: './results/' input: 'data' <...>
You can also generate such
JSON files via nf-core/launch.
The pipeline will generate one heatmap each, for the Housekeeping-normalized and non-Housekeeping-normalized data. These heatmaps will also be included in the MultiQC report. Per default the heatmap will include all endogenous genes. If you want to generate the heatmap for a subset of genes, please specify a
yml file using the parameter
--heatmap_genes_to_filter with the following format:
- geneA - geneB ...
⚠️ If you want to use other metadata in your samplesheet than the one shown in the section Full samplesheet, please make sure to specify the
ymlfile with all endogenous genes or a subset of it.
The pipeline can compute gene scores for arbitrary sets of genes. It automatically checks for the set of desired genes to be present in the data, e.g. you cannot specify a set of genes that is not also present and measured in your nCounter experiment. Furthermore, the algorithm / method of choice can be adjusted - available options are:
The recommendation is to use PLAGE or PLAGE in the directed form (default) for Nanostring nCounter data. You can simply start the analysis by supplying an appropriate YAML description with the desired gene and the required genes, e.g. to compute the MPAS score, supply this as a yaml using the option
MPAS: - PRY2 - SPRY4 - ETV4 - ETV5 - DUSP4 - DUSP6 - CCND1 - EPHA2 - EPHA4
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/nanostring
It is 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 nf-core/nanostring 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
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 reproducbility, you can use share and re-use parameter files to repeat pipeline runs with the same settings without having to write out a command with every single parameter.
💡 If you wish to share such profile (such as upload as supplementary material for academic publications), make sure to NOT include cluster specific paths to files, nor institutional specific profiles.
Core Nextflow arguments
*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, Apptainer, 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.
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.
-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 enviroment.
- A profile with a complete configuration for automated testing
- Includes links to test data so needs no other parameters
- A generic configuration profile to be used with Docker
- A generic configuration profile to be used with Singularity
- A generic configuration profile to be used with Podman
- A generic configuration profile to be used with Shifter
- A generic configuration profile to be used with Charliecloud
- A generic configuration profile to be used with Apptainer
- 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.
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.
Specify the path to a specific config file (this is a core Nextflow command). See the nf-core website documentation for more information.
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 steps in the pipeline, if the job exits with any of the error codes specified here it will automatically be resubmitted with higher requests (2 x original, then 3 x original). If it still fails after the third attempt then the pipeline execution is stopped.
In some cases you may wish to change which container or conda environment a step of the pipeline uses 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.
Running in the background
Nextflow handles job submissions and supervises the running jobs. The Nextflow process must run until the pipeline is finished.
-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
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