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Experimental design in an oligonucleotide synthesis factory using numerical simulations in Python and pandas

Aaron Wiegel

Regardless of the application, calculating a particular statistic and associated p-value is not necessarily the biggest challenge in designing an experiment, especially given the availability of open source software packages such as scipy and statsmodels in Python. Instead, ensuring that the assumptions required for a statistical test are actually satisfied by the data is far more challenging. Thankfully, with an existing data source, the sample method for a dataframe in pandas can be used to create simple numerical simulations to test these assumptions with real data. Using such numerical simulations on data from an oligonucleotide synthesis factory, I discuss the fundamental concepts of sampling, statistical power, and experimental design in the context of my work as a data scientist at Synthego, a biotech manufacturing startup.

Speaker: Aaron Wiegel, Synthego

Wednesday, 02/06/19


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Tan Hall

UC Berkeley
Room 775A
Berkeley, CA 94720