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© Christelle Durand
Microscopie d'un neurone. Le marquage jaune montre les synapses.
Publication : GigaScience

Experimenting with Reproducibility: a case study of Robustness in Bioinformatics

Scientific Fields
Diseases
Organisms
Applications
Technique

Published in GigaScience - 28 Jun 2018

Kim YM, Poline JB, Dumas G

Link to Pubmed [PMID] – 29961842

Gigascience 2018 Jun;

Reproducibility has been shown to be limited in many scientific fields. This question is a fundamental tenet of scientific activity, but the related issues of reusability of scientific data are poorly documented. Here, we present a case study of our difficulties to reproduce a published bioinformatics method even though code and data were available. First, we tried to re-run the analysis with the code and data provided by the authors. Second, we reimplemented the whole method in a Python package to avoid dependency on a MATLAB license and ease the execution of the code on a HPCC (High-Performance Computing Cluster). Third, we assessed reusability of our reimplementation and the quality of our documentation, testing how easy it would be to start from our implementation to reproduce the results. In a second section, we propose solutions from this case study and other observations to improve reproducibility and research efficiency at the individual and collective level.While finalizing our code, we created case specific documentation and tutorials for the associated Python package StratiPy. Readers are thus invited to experiment with our reproducibility case study by generating the two confusion matrices of Fig 3 (see more in 2.2.2).Here we decided to propose two options: 1) a step-by-step process to follow in a Jupyter/IPython notebook; or 2) a Docker container ready to be built and run.

https://www.ncbi.nlm.nih.gov/pubmed/29961842