Predicting computational reproducibility of data analysis pipelines in large population studies using collaborative filtering
This work addresses the cumbersome process of reproducibility evaluation for large-scale data analyses, offering a domain-specific solution that is incremental in nature.
The authors tackled the problem of efficiently evaluating computational reproducibility of data analysis pipelines in large population studies by proposing a collaborative filtering method with constrained training set construction, achieving good accuracy with a specific sampling strategy and enabling substantial speedup with minimal accuracy loss.
Evaluating the computational reproducibility of data analysis pipelines has become a critical issue. It is, however, a cumbersome process for analyses that involve data from large populations of subjects, due to their computational and storage requirements. We present a method to predict the computational reproducibility of data analysis pipelines in large population studies. We formulate the problem as a collaborative filtering process, with constraints on the construction of the training set. We propose 6 different strategies to build the training set, which we evaluate on 2 datasets, a synthetic one modeling a population with a growing number of subject types, and a real one obtained with neuroinformatics pipelines. Results show that one sampling method, "Random File Numbers (Uniform)" is able to predict computational reproducibility with a good accuracy. We also analyze the relevance of including file and subject biases in the collaborative filtering model. We conclude that the proposed method is able to speedup reproducibility evaluations substantially, with a reduced accuracy loss.