Growth Score: A single metric to define growth in 96-well phenotype assays
- Published
- Accepted
- Subject Areas
- Computational Biology, Microbiology
- Keywords
- bacterial growth, bacteria phenotype, phenotype microarrays
- Copyright
- © 2018 Cuevas et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2018. Growth Score: A single metric to define growth in 96-well phenotype assays. PeerJ Preprints 6:e26469v1 https://doi.org/10.7287/peerj.preprints.26469v1
Abstract
High-throughput phenotype assays are a cornerstone of systems biology as they allow direct measurements of mutations, genes, strains, or even different genera. High-throughput methods also require data analytic methods that reduce complex time-series data to a single numeric evaluation. Here, we present the Growth Score, an improvement on the previous Growth Level formula. There is strong correlation between Growth Score and Growth Level, but the new Growth Score contains only essential growth curve properties while the formula of the previous Growth Level was convoluted and not easily interpretable. Several programs can be used to estimate the parameters required to calculate the Growth Score metric, including our PMAnalyzer pipeline.
Author Comment
This is a submission to PeerJ for review.
Supplemental Information
Growth curve simulation Python script
Growth curve simulation Python script without plotting functions.
Jupyter Notebook growth curve simulations
Jupyter Notebook version of the growth curve simulation script, along with Seaborn plotting functions.
Jupyter Notebook PDF
The PDF version of the growth curve simulation Jupyter Notebook.