RT Journal Article
SR Electronic
T1 To bin or not to bin: analyzing single-cell growth data
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.07.27.453901
DO 10.1101/2021.07.27.453901
A1 Prathitha Kar
A1 Sriram Tiruvadi-Krishnan
A1 Jaana Männik
A1 Jaan Männik
A1 Ariel Amir
YR 2021
UL http://biorxiv.org/content/early/2021/07/27/2021.07.27.453901.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.