Collection of high-throughput data has become prevalent in biology. Large datasets allow the use of statistical constructs such as binning and linear regression to quantify relationships between variables and hypothesize underlying biological mechanisms based on it. We discuss several such examples in relation to single-cell data and cellular growth. In particular, we show instances where what appears to be ordinary use of these statistical methods leads to incorrect conclusions such as growth being non-exponential as opposed to exponential and vice versa. We propose that the data analysis and its interpretation should be done in the context of a generative model, if possible. In this way, the statistical methods can be validated either analytically or against synthetic data generated via the use of the model, leading to a consistent method for inferring biological mechanisms from data. On applying the validated methods of data analysis to infer cellular growth on our experimental data, we find the growth of length in E. coli to be non-exponential. Our analysis shows that in the later stages of the cell cycle the growth rate is faster than exponential.
All data generated during this study are deposited in Dataverse-:Kar, Prathitha; Tiruvadi-Krishnan, Sriram; Männik, Jaana; Männik, Jaan; Amir, Ariel, 2021, "Distinguishing different modes of growth using single-cell data", https://doi.org/10.7910/DVN/BNQUDW, Harvard Dataverse, V1
Distinguishing different modes of growth using single-cell dataHarvard Dataverse, V1.
Data from long-term growth data of Escherichia coli at a single-cell levelFigshare, doi: 10.6084/m9.figshare.c.3493548.
- Jaan Männik
- Jaan Männik
- Ariel Amir
- Prathitha Kar
- Prathitha Kar
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
- Gordon J Berman, Emory University, United States
© 2021, Kar et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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