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Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves.

Sprouffske K, Wagner A - BMC Bioinformatics (2016)

Bottom Line: The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN).The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

View Article: PubMed Central - PubMed

Affiliation: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.

ABSTRACT

Background: Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis.

Results: Growthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.

Conclusions: Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

No MeSH data available.


Comparisons of Growthcurver’s growth curve metrics for experimental data from 937 replicate E. coli populations. We plotted the growth curve metrics in a pairwise fashion to identify correlations between metrics. The metrics are listed in the diagonal (growth rate, doubling time, the logarithm of the initial population size, the area under the logistic curve, the area under the experimentally-measured curve, and the carrying capacity). We plotted the pairwise comparisons in the lower diagonal; for example, in the panel comparing growth rate and doubling time, each point is the growth rate and doubling time obtained from Growthcurver for a single experimental replicate. The Spearman correlation of any panel in the lower diagonal is reported in the upper diagonal
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Fig1: Comparisons of Growthcurver’s growth curve metrics for experimental data from 937 replicate E. coli populations. We plotted the growth curve metrics in a pairwise fashion to identify correlations between metrics. The metrics are listed in the diagonal (growth rate, doubling time, the logarithm of the initial population size, the area under the logistic curve, the area under the experimentally-measured curve, and the carrying capacity). We plotted the pairwise comparisons in the lower diagonal; for example, in the panel comparing growth rate and doubling time, each point is the growth rate and doubling time obtained from Growthcurver for a single experimental replicate. The Spearman correlation of any panel in the lower diagonal is reported in the upper diagonal

Mentions: The growth rate is often used to summarize growth curve data, and we wondered to what extent the growth rate correlates with other metrics that summarize growth (Fig. 1). The growth rate correlates perfectly with the doubling time (Spearman, ρ=−1,p<2.2×10−16), which is unsurprising since the definition of doubling time relies on growth rate (see Implementation). The growth rate also correlates with the initial population size (Spearman, ρ=−0.88,p<2.2×10−16), the area under the curve (Spearman, ρ=0.57,p<2.2×10−16), and the carrying capacity (Spearman, ρ=0.32,p<2.2×10−16). The area under the curve is a promising metric to summarize a growth curve because it integrates the contributions of the initial population size, growth rate, and carrying capacity into a single value, and emphasizes growth rate (Spearman correlation between area under the curve and growth rate, ρ=0.57,p<2.2×10−16) and carrying capacity (Spearman correlation between area under the curve and carrying capacity, ρ=0.81,p<2.2×10−16). A viable non-parametric approach is to use the empirical area under the curve, which is correlated with the area under the curve (Spearman, ρ=1,p<2.2×10−16).Fig. 1


Growthcurver: an R package for obtaining interpretable metrics from microbial growth curves.

Sprouffske K, Wagner A - BMC Bioinformatics (2016)

Comparisons of Growthcurver’s growth curve metrics for experimental data from 937 replicate E. coli populations. We plotted the growth curve metrics in a pairwise fashion to identify correlations between metrics. The metrics are listed in the diagonal (growth rate, doubling time, the logarithm of the initial population size, the area under the logistic curve, the area under the experimentally-measured curve, and the carrying capacity). We plotted the pairwise comparisons in the lower diagonal; for example, in the panel comparing growth rate and doubling time, each point is the growth rate and doubling time obtained from Growthcurver for a single experimental replicate. The Spearman correlation of any panel in the lower diagonal is reported in the upper diagonal
© Copyright Policy - OpenAccess
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4837600&req=5

Fig1: Comparisons of Growthcurver’s growth curve metrics for experimental data from 937 replicate E. coli populations. We plotted the growth curve metrics in a pairwise fashion to identify correlations between metrics. The metrics are listed in the diagonal (growth rate, doubling time, the logarithm of the initial population size, the area under the logistic curve, the area under the experimentally-measured curve, and the carrying capacity). We plotted the pairwise comparisons in the lower diagonal; for example, in the panel comparing growth rate and doubling time, each point is the growth rate and doubling time obtained from Growthcurver for a single experimental replicate. The Spearman correlation of any panel in the lower diagonal is reported in the upper diagonal
Mentions: The growth rate is often used to summarize growth curve data, and we wondered to what extent the growth rate correlates with other metrics that summarize growth (Fig. 1). The growth rate correlates perfectly with the doubling time (Spearman, ρ=−1,p<2.2×10−16), which is unsurprising since the definition of doubling time relies on growth rate (see Implementation). The growth rate also correlates with the initial population size (Spearman, ρ=−0.88,p<2.2×10−16), the area under the curve (Spearman, ρ=0.57,p<2.2×10−16), and the carrying capacity (Spearman, ρ=0.32,p<2.2×10−16). The area under the curve is a promising metric to summarize a growth curve because it integrates the contributions of the initial population size, growth rate, and carrying capacity into a single value, and emphasizes growth rate (Spearman correlation between area under the curve and growth rate, ρ=0.57,p<2.2×10−16) and carrying capacity (Spearman correlation between area under the curve and carrying capacity, ρ=0.81,p<2.2×10−16). A viable non-parametric approach is to use the empirical area under the curve, which is correlated with the area under the curve (Spearman, ρ=1,p<2.2×10−16).Fig. 1

Bottom Line: The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN).The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

View Article: PubMed Central - PubMed

Affiliation: Department of Evolutionary Biology and Environmental Studies, University of Zurich, Winterthurerstrasse 190, Zurich, 8057, Switzerland.

ABSTRACT

Background: Plate readers can measure the growth curves of many microbial strains in a high-throughput fashion. The hundreds of absorbance readings collected simultaneously for hundreds of samples create technical hurdles for data analysis.

Results: Growthcurver summarizes the growth characteristics of microbial growth curve experiments conducted in a plate reader. The data are fitted to a standard form of the logistic equation, and the parameters have clear interpretations on population-level characteristics, like doubling time, carrying capacity, and growth rate.

Conclusions: Growthcurver is an easy-to-use R package available for installation from the Comprehensive R Archive Network (CRAN). The source code is available under the GNU General Public License and can be obtained from Github (Sprouffske K, Growthcurver sourcecode, 2016).

No MeSH data available.