Abstracted repository-centric analysis framework
Project description
Abstraction of Repository-Centric ANAlysis (Arcana) is Python framework for “repository-centric” analyses of study groups (e.g. NeuroImaging studies)
Arcana interacts closely with a repository, storing intermediate outputs, along with the parameters used to derive them, for reuse by subsequent analyses. Repositories can either be XNAT repositories or (http://xnat.org) local directories organised by subject and visit, and a BIDS module (http://bids.neuroimaging.io/) is planned as future work.
Analysis workflows are constructed and executed using the Nipype package, and can either be run locally or submitted to HPC schedulers using Nipype’s execution plugins. For a requested analysis output, Arcana determines the required processing steps by querying the repository to check for missing intermediate outputs before constructing the workflow graph. When running in an environment with the modules package installed, Arcana manages the loading and unloading of software modules per pipeline node.
Design
Arcana is designed with an object-oriented philosophy, with the acquired and derived data sets along with the analysis pipelines used to derive the derived data sets encapsulated within “Study” classes.
The Arcana package itself only provides the abstract Study and MultiStudy base classes, which are designed to be sub-classed to provide specialised classes representing the analysis that can be performed on specific types of data (e.g. FmriStudy, PetStudy). These specific classes can then be sub-classed further into classes that are specific to a particular study, and integrate the complete workflow from preprocessing to statistical analysis.
Installation
Arcana can be installed using pip:
$ pip install arcana
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