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Rescuing and Exploring Complex Life Science Data


Often we have no choice but to work with messy, difficult data. I describe the Python-based approaches used to rescue and repair a complex malformed dataset (using csvkit and a rule-driven sanitisation approach), mount it in a new user-friendly db (using pycap) before exploration (using py4neo). I finish by reflecting on Python’s “gaps” as concerns life science/ biomedical analytical tools.


Everyone complains about messy, difficult datasets but often we have no choice but to work with them. In 2014, I was charged with the “informatic rescue” of the data for a large trans-European epidemiological trial, where the challenges were (1) to extract and make useable the complicated but malformed patient data in a remote and idiosyncratic database, (2) make this available and regularly updated in a user-friendly system, (3) integrate several other data sources and finally (4) explore the data for research purposes.

Here I describe the Python-based approach I used. Starting with csvkit to recreate the original legacy database for direct examination and manipulation, malformed data was transformed by a pipeline of rule-driven sanitisation before being subjected to validation via another pipeline of rules. I describe how pycap and REDCap were used to make an easily updatable and user- friendly database, and how this was leveraged in merging other datasets. I show how this data was integrated with associated and complex datasets (analytical and genomic) and explored in a graph database using py4neo.

Finally, I reflect on the gaps in Python’s life science and biomedical analytical offerings, including why Excel spreadsheets are here to stay, if our current IDEs are good enough and whether developers are the enemy of the good enough.


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