Creating a Nextflow Data Pipeline


Pipelines / Workflows

It’s possible to convert a Viash component into a NextFlow module. Viash uses NextFlow’s DSL2 for this, effectively creating modules that can be imported in a pipeline definition that deals with the logic of the pipeline rather than the low-level machinery.

When it comes to this low-level machinery and the way Viash creates a module, we refer to the step-by-step introduction about DiFlow.


NextFlow, as any other pipeline platform is able to run tasks in parallel if the pipeline logic allows for that. In order to keep different parallel branch unique, we add a unique identifier id. This identifier can be a sample identifier, or a plate id (sequencing), versions of reference files to consider, etc.

In a pipeline, one is often interested in running some computation on different datasets, inputs or parts of an input. This means that obviously we need to keep track of where the input chunks are located. But more importantly, we can not just simply name an output file because multiple parallel processes might just overwrite each other’s output files.

Output Filenames

Therefore, it’s important to keep output files distinct across different steps of the pipeline but also between different parallel runs. In order to assure this, a module will define its own output file name. It is constructed from 3 ingredients:

  1. The unique id of the data going into the process
  2. The name of the current component/task
  3. An extension

The extension is derived from the component configuration if a default: ... attribute is present for the corresponding output argument. If no such default value is provided, the name of the option is used.

As an example, the following argument for a component comp

name: --log
type: file
direction: Output

will result in <id>.comp.log, while the following

name: --log
type: file
direction: Output
default: log.txt

will become in <id>.comp.txt.

The config can define a directory as output as well, it will be named accordingly.

Remark: If the output is a directory, type: file should still be used, but the corresponding script/code should take care of writing the content to that directory.


How to specify multiple inputs?

If a component deals with just one input file, that input file should be provided as the second element in the DiFlow triplet. In other words, if this is the first component in a (sub)workflow, two options are available:

Channel.fromPath(<...>).map{ file -> [ <id>, file, params ] }


Channel.from(<...>).map{ filename -> [ <id>, file(filename), params ] }

It is crucial that this second element in the triplet is of type Path.

If multiple inputs are to be provided corresponding to the same option for the underlying process or tool a List of Path objects can be provided.

For example, say we ran multiple parallel workflows for a single sample and want to join the result of that. The way to express this in NextFlow would be something like:

concatenate_ = singleSample_ \
    | toList \
    | map{ it -> [ it.collect{ b -> b[0]}, it.collect{ a -> a[1] }, params ]} \
    | concatenate

In other words, we pass a List of Path objects to the concatenate module.

In some cases, multiple input arguments deal with different input files, for instance fastq files and a reference file for mapping and counting. One can pass this to the concatenation module by means of a Map. This approach, for instance can be used to merge meta information (from a file) to an h5ad file:

    singleSample_ = input_ \
        | combine(meta_) \
        | map{ id, output, params, meta ->
            [ id, [ "input" : output, "meta" : meta ], params ]
        } \
        | annotate

Where the meta_ Channel points to the meta file to be used.

In other words, we either provide a List of Path values or in the case multiple options take different files we use a HashMap.

Remark: Be sure to mark the arguments at hand as being of type: file and direction: input.

Multiple outputs

Internally, DiFlow uses a similar approach to keeping track of outputs as discussed for inputs. What comes out of a module, however, is slightly different. Since a workflow can not emit a multi-channel object, we are forced to put all outputs on the same Channel and so we use a Map again to distinguish both. This is only done for multiple outputs, though.

By means of an example: Say a module outputs one file, then the triplet that is returned from the module looks like this:

[ <id>, file, params ]

If our tools has two output files, say for instance outputfile.txt and logfile.txt (as indicated by the command line for the tool that looks for instance like this: .... --output outputfile.txt --log logfile.txt), we still get one Channel back, but on that Channel there are now two events and those look like this:

[ <id>, [ output: outputfile.txt ], params ]
[ <id>, [ log: logfile.txt ], params ]

It’s up to the receiving end of the module to split this downstream. The implicit workflow defined in all the generated module contains some example code to that, for instance:

result \
  | filterOutput \
  | view{ "Output for output: " + it[1] }

result \
  | filterLog \
  | view{ "Output for log: " + it[1] }

Where the filterLog process for instance is defined like so:

// A process that filters out output from the output Map
process filterOutput {

    tuple val(id), val(input), val(_params)
    tuple val(id), val(output), val(_params)
    output = input["output"]


Alternatively, one could also use methods on the Channel itself:

result \
  | filter{ it[1].keySet().contains("output") }
  | map{ [ it[0], it[1]["output"], it[2] ] }
  | view{ "Output for log: " + it[1] }

One more option is to use the branch or multiMap Channel forking operators in NextFlow.

Access arguments from the nextflow CLI (v0.4.1+)

We provide a way to access the argument values for all the arguments for every component from the NextFlow CLI. All components store their respective arguments under a component-specific key that corresponds to the component name.

Furthermore, the value that will effectively be passed to the process depends on the following attributes for an argument:

required: true/false
default: ...

If an argument is required: true, it can have

  • a default: ... value: It will use this default value on the CLI unless you override it on the CLI
  • no default value: In this case, one has to provide a value when starting a NextFlow pipeline. Otherwise Viash will give a warning and NXF will throw an error.

If an argument is required: false, it can have

  • a default: ... value: It will use this default value unless you override it from the CLI
  • no default value: In this case, this argument will not be present on the CLI, but it can still be overridden from the CLI

Please refer to this issue for more information.

Given the above, its possible to override any of these using the following scheme:

--<component_name>__<argument_name> <value>

For instance:

nextflow run --filter__threshold 0.2