Distinguishing different modes of growth using single-cell data.

ABSTRACT: 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

SUBMITTER: Kar P 

PROVIDER: S-EPMC8727026 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature