Adding a new subworkflow to nf-neuro

Generate the template

First verify you are located at the root of this repository (not in subworkflows), then run the following interactive command :

nf-core subworkflows create

It will ask you to give a name for your subworkflow and an author name (use your github username). Alternatively, you can use the following command to supply directly those informations :

nf-core subworkflows create <name> --author <author>

The command will generate a new subworkflow template in the ./subworkflows/nf-neuro/<name_of_your_workflow> directory.

Edit the template

Edit main.nf

You can’t select an empty template when you generate a new subworkflow, so the template is based on nf-core. You will need to replace some of their sections for your use case:

• Remove the different comment lines.
• Include your modules into your subworkflows (a subworkflow should include at least two modules). Remove the modules { SAMTOOLS_SORT} and { SAMTOOLS_INDEX } then includes yours with the good pathway:

include { <MODULES>	} from '../../../modules/nf-neuro/<category>/<tool>/main'

include { <SUBWORKFLOW> } from '../<subworkflow>/main'

Define your subworkflow inputs

A workflow can declare one or more input channels using the take keyword. If some of your input channels are optional, add an optional tag after the channel specification. Multiple inputs must be specified on separate lines:

take:
    channel_data1  // channel: [ val(meta), [ data1 ] ]
    channel_data2  // channel: [ val(meta), [ data2 ] ], optional

Fill the main: section

Compose your workflow using the different modules and workflows you’ve included above. For inputs channels, use it as follows:

<MODULE1> (channel_data1)

To connect two modules together, you need to create a channel which takes one of the outputs of the first module and feeds it to the other. To do this, use the .out attribute and select the desired output by name :

channel_module2 = <MODULE1>.out.<output>
<MODULE2> (channel_module2)

To assemble the outputs of one or multiple modules together in a new channel, use the join, combine and groupTuple operators. For example :

channel_module3 = <MODULE2>.out.<output>.join(channel_data1).join(channel_data2)
<MODULE3> (channel_module3)

To select a subset of values emitted by a channel (e.g. a channel emits tuples of the shape [meta, out1, out2, out3], out2 and out3 are desired but not out1), use the map operator, for example :

channel_subset = channel_data.map{ meta, out1, out2, out3 -> [meta, out2, out3] }

For validation, you need to collect the version files of the modules and subworkflows included in yours. The first thing to do in the main is to create an empty channel:

ch_versions = Channel.empty()

Then, after each module call, add its version file into the channel:

channel_module2 = <MODULE1>.out.<output>
<MODULE2> (channel_module2)
ch_versions = ch_versions.mix(<MODULE2>.out.versions.first())

Define your Workflow outputs

Once the main finished you can define the output that you want from the different modules or workflows, be sure to assign just one output per channel. Please list as many outputs as possible for your workflow, so that it can be better reused and adapted.

A workflow can declare one or more output channels using the emit keyword.

emit:
    output1 = <MODULE1>.out.<output> // channel: [ val(meta), [ output ] ]
    output2 = <MODULE2>.out.<output> // channel: [ val(meta), [ output ] ]

Don’t forget to also define the output for the version file :

versions = ch_versions // channel: [ versions.yml ]

Edit meta.yml

Fill the sections you find relevant. There is a lot of metadata in this file, but you don’t need to specify them all. Provide at least 3 relevant keywords and list all modules and subworkflows used in the components section. List all inputs and outputs in the order in which you defined them. Give a complete description of the subworkflow, describing all potential uses and variations of inputs and their effects on expected outputs.

Create test cases

Adding tests to your subworkflow is near identical to modules. For detailed instructions, please see here.

Lint your code

Run prettier on your new module, through the nf-core command line :

nf-core subworkflows lint <subworkflow>

and fix all errors and as many warnings as possible. If at any point you modified files outside of your module’s directory, those also need to be linted. To do so, run the following commands at the root of the repository :

prettier --check .
editorconfig-checker

You’ll get lists of syntax errors to fix from both commands. For prettier, you can run prettier --write . to fix most of them automatically. For editorconfig-checker, you’ll have to fix them manually.

Submit your PR

Open a PR to the nf-neuro repository main branch. We’ll test everything, make sure it’s working and that code follows standards.

Once LGTM has been declared, wave to the maintainers and look at your hard work paying off.

PR merged !