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Interactively AUDIT Your Growth Curves with a Suite of R Packages.

Nicolas P J CoutinGuri GiaeverCorey Nislow
Published in: G3 (Bethesda, Md.) (2020)
Bottlenecks often occur during data analysis when studying microbial growth in liquid culture at large scale. A researcher can collect thousands of growth curves, repeated measures of a microbial liquid culture, at once in multiple micro titer plates by purpose-built robotic instruments. However, it can be difficult and time-consuming to inspect and analyze these data. This is especially true for researchers without programming experience. To enable this researcher, we created and describe an interactive application: Automated Usher for Data Inspection and Tidying (AUDIT). It allows the user to analyze growth curve data generated from one or more runs each with one or more micro titer plates alongside their experimental design. AUDIT covers input, pre-processing, summarizing, visual exploration and output. Compared to previously available tools AUDIT handles more data, provides live previews and is built from individually re-usable pieces distributed as R packages.
Keyphrases
  • data analysis
  • electronic health record
  • big data
  • microbial community
  • machine learning
  • ionic liquid
  • deep learning
  • minimally invasive
  • patient reported outcomes