The concept of two-tiered pipelines
In our experience, we are typically interested in running two different types of commands: Those that operate on each sample independently, and those that operate on all samples simultaneously. Since sample-independent pipelines can be easily parallelized by sample, we distinguish these.
Looper divides pipelines into two types: sample pipelines and project pipelines.
This philosophy is conceptually similar to the MapReduce programming model, which applies a split-apply-combine strategy. In the case of running pipelines on sample-intensive research projects, we split the project into samples and apply the first tier of processing (the sample pipeline). We then combine the results in the second tier of processing (the project pipeline).
Looper doesn't require you to use this two-stage system, but it simply makes it easy to do so. Many pipelines operate only at the sample level and leave the downstream cross-sample analysis to the user.
The typical use case is sample-level pipelines. These are run with
looper run. Pipeline interface defining a sample pipeline must to include
pipeline_type: "sample" statement.
Project pipelines, identified by
pipeline_type: "project" statement in the pipeline interface, will be run with
looper runp (where the p stands for project). Running a project pipeline operates in almost exactly the same way as the sample pipeline, with 2 key exceptions: First, instead of creating a separate command for every sample, the
looper runp will only create a single command per pipeline for the project. And second, the command template itself will not have access to a
sample namespace representing a particular sample, since it's not running on a particular sample; instead, it will have access to a
samples (plural) namespace, which contains all the attributes from all the samples.
In a typical workflow, a user will first run the samples individually using
looper run, and then, if the pipeline provides one, will run the project component using
looper runp to summarize or aggregate the results into a project-level output.