Growth Score: A single metric to define growth in 96-well phenotype assays
A peer-reviewed article of this Preprint also exists.
Author and article information
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.
Cite this as
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 PeerJ Preprints 6:e26469v1 https://doi.org/10.7287/peerj.preprints.26469v1note This preprint is not peer-reviewed. You may wish to reference the subsequent peer-reviewed version of this article.
Author comment
This is a submission to PeerJ for review.
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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.
Additional Information
Competing Interests
The authors declare that they have no competing interests.
Author Contributions
Daniel A Cuevas conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, wrote the paper, prepared figures and/or tables, reviewed drafts of the paper.
Robert A Edwards analyzed the data, wrote the paper, reviewed drafts of the paper.
Funding
This work is supported by the National Science Foundation (CNS-1305112 and MCB-1330800). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
